Learning in Information-Rich Environments: I-LEARN and the Construction of Knowledge in the 21st Century

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Learning in Information-Rich Environments

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Delia Neuman

Learning in Information-Rich Environments I-LEARN and the Construction of Knowledge in the 21st Century

Delia Neuman Ph.D. College of Information Science and Technology Drexel University 3141 Chestnut Street Philadelphia, PA 19104-2875 USA [email protected]

ISBN 978-1-4419-0578-9 e-ISBN 978-1-4419-0579-6 DOI 10.1007/978-1-4419-0579-6 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011920812 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

For Michael

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Preface

We are all learners, and in the twenty-first century learners must navigate a swirling sea of information to make sense of the world. From the time the clock radio clicks on in the morning to the time the last text message is received for the night, we are flooded with stimuli • That ask us to pay attention (or give us a way to vegetate). • That invite us to distinguish the useful from the useless (or allow us to lose ourselves in the fog). • That call on us to create new products of our own (or encourage us to be passive consumers of others’ ideas). The stimuli come in all formats – print, visuals, music, talk, exhibits, and even odors. They come through avenues as familiar as the daily newspaper and as exotic as the latest social-media site or “app.” The one characteristic the formats and avenues have in common is that they all provide some kind of information. Together, they offer a tsunami of facts, ideas, and opinions that we can access, evaluate, and use to build an understanding of the world and of ourselves – that is, to learn. The amount and range of information available to us today is unprecedented. Phrases like “the information revolution,” “the information (or knowledge) society,” “the knowledge economy,” and similar expressions underscore the truism that our society has been transformed by virtually instantaneous access to virtually unlimited stores of information. Thomas Friedman was among the first to tell us that the world is flat (2005, 2007) and that we must devise new political and economic understandings based on the ceaseless communication of information from all corners of the world. Governments continue to tell us that information relating to national security is so time-sensitive that we must allow new kinds of surveillance to keep society safe. Teenage subscribers to social networks not only access information but enter text and video images and publish them widely – becoming the first adolescents in history to be creators as well as consumers of vast quantities of information. If the characteristics of “the information age” demand new conceptions of commerce, national security, and publishing – among other things – it is logical to assume that they carry implications for education as well. In fact, a good deal has vii

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been written over the last several decades about how education as a whole must transform its structure and curriculum to accommodate the possibilities offered by new technologies. Far less has been written, however, about the specific implications of these technologies – and the information they allow students (and all learners) to access and create – for the central purpose of education: learning. What does “learning” mean in an information-rich environment? What are its characteristics? What kinds of tasks should it involve? What concepts, strategies, attitudes, and skills must educators and students master to learn effectively and efficiently in such an environment? How can researchers, theorists, and practitioners foster the wellfounded and widespread development of such key elements of the learning process? This book explores these questions and suggests some tentative answers. Chapter 1 portrays information not just as a collection of facts, ideas, and opinions but as a tool for learning that provides the basic building blocks for critical thinking and problem solving. Chapters 2 and 3 define and describe the formal and informal information-rich environments that surround us and show how their evolution suggests a need for an expanded conception of learning itself. Chapter 4 paints a picture of learners as “information users” and describes their needs and abilities for learning in information-rich environments, particularly as the importance of such learning gains increasing attention. Chapter 5 draws on the core ideas of information literacy to provide a framework for learning in the kinds of dynamic, information-rich environments available today and to offer the author’s I-LEARN model as a way to guide information-based learning at the highest levels. Chapter 6 closes the loop about learning in information-rich environments by discussing contemporary assessment approaches and describing how the model can serve as a tool for evaluating learning in both formal and informal settings. In the twenty-first century, information in all its vastness and variety provides the raw material for the kind of learning that all of us must master as we encounter new realities in society and in our personal lives. Indeed, the process of accessing, evaluating, using, and creating information constitutes the “authentic learning” that contemporary education promotes and that all of us must pursue throughout our lives; the skills involved in doing so are the “skills for twenty-first-century learning” that are so widely touted today. By exploring some of the key ideas and issues related to learning with information at this early stage of the information age, this book attempts to provide some insights and suggestions that will help educators and those we serve make steady progress in that pursuit. I am indebted to many people for insights and encouragement that played an important part in creating this book: to Sister Ann Edward Bennis, SSJ, who introduced me to the joys and rigors of scholarship; to my doctoral adviser Keith Hall, whose vision for technology as a learning tool helped shape my own; and to friends, colleagues, and students at both Drexel University and the University of Maryland who helped me reach and refine the ideas presented here: George Abraham, Eileen Abels, Jinsoo Chung, Gary Marchionini, Sheri Massey, Katherine McCain, Kara Reuter, Dagobert Soergel, Adam Townes, Patricia Verdines, and Philip Wu. I also owe my thanks to Ellen Coughlin, my friend and editor, to Kara Howland,

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my graphic designer, to Brenna Pelligrini for “technical assistance,” and to all those who endured my musings – and my frustrations – as the writing progressed. Above all, I am deeply grateful to my husband Michael Neuman for his insights, his questions, his humor, his generosity, and his extraordinary patience. Any errors in the book are, of course my own; any value it offers is attributable to many. Philadelphia, PA

Delia Neuman, Ph.D

Reference Friedman, T. (2005, 2007). The world is flat. New York: Farrar, Strauss, and Giroux.

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Contents

1 Information as a Tool for Learning.......................................................... 1.1 What Is Information? The View from Information Studies................ 1.2 What Is Information? The View from Instructional Design and Development.................................................................... 1.2.1 The Knowledge Dimension.................................................... 1.2.2 The Cognitive Process Dimension.......................................... 1.3 The Views Converge........................................................................... 1.4 What Is Learning?............................................................................... 1.4.1 Early Learning Theory............................................................ 1.4.2 Learning Theory Today........................................................... 1.5 Information and Learning................................................................... 1.6 Conclusion.......................................................................................... References....................................................................................................

4 5 7 9 9 10 10 11 13 14

2 Information-Rich Environments: Single-Sense, Multisensory, and Interactive........................................................................................... 2.1 What is an Information-Rich Environment?....................................... 2.2 Information Objects in Information-Rich Environments.................... 2.2.1 Single-Sense Information Objects.......................................... 2.2.2 Multisensory Information Objects.......................................... 2.2.3 Interactive Information Objects.............................................. 2.2.4 Additional Affordances of Digital Information Objects......... 2.3 Conclusion.......................................................................................... References....................................................................................................

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3 Information and Communication Technologies: The Penultimate Interactive Information-Rich Environment............... 3.1 The ICT Environment: Interactivity, Information, and Learning....... 3.2 Learning Affordances of the ICT Environment.................................. 3.2.1 Access to Information Objects Within the Internet and the World Wide Web..................................... 3.2.2 Learning Affordances Unique to ICTs....................................

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3.3 Theory and Research on ICTs’ Learning Affordances....................... 3.3.1 Distributed Processing and Collaboration.............................. 3.3.2 Discourse Strategies and Distributed Processing.................... 3.3.3 Collaboration and Discourse Strategies.................................. 3.4 Conclusion.......................................................................................... References....................................................................................................

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4 Today’s Learners and Learning with Information................................. 4.1 Research from Information Studies.................................................... 4.2 The “Information Literacy” Movement.............................................. 4.3 Learning with Information Today....................................................... 4.4 Directions for the Future..................................................................... 4.5 Filling in the Gaps............................................................................... 4.6 The Contributions of Instructional Design and Development............ 4.7 The Contributions of Information Studies.......................................... 4.8 Information Studies Meets Instructional Design and Development.................................................................... 4.8.1 Research Issues....................................................................... 4.8.2 Theoretical Frameworks......................................................... 4.9 Conclusion.......................................................................................... References....................................................................................................

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5 I-LEARN: A Model for Learning with Information.............................. 5.1 Information Literacy and Instruction.................................................. 5.2 The I-LEARN Model: Introduction.................................................... 5.3 The I-LEARN Model: The Theory..................................................... 5.3.1 The Knowledge Dimension.................................................... 5.3.2 The Cognitive Process Dimension.......................................... 5.3.3 Types of Knowledge, Cognitive Processes, and Information Literacy........................................................ 5.4 The I-LEARN Model.......................................................................... 5.4.1 Stage 1: Identify...................................................................... 5.4.2 Stage 2: Locate........................................................................ 5.4.3 Stage 3: Evaluate..................................................................... 5.4.4 Stage 4: Apply......................................................................... 5.4.5 Stage 5: Reflect....................................................................... 5.4.6 Stage 6: kNow......................................................................... 5.5 Conclusion.......................................................................................... References....................................................................................................

85 86 87 90 91 93 96 97 98 99 101 105 110 112 113 114

6 I-LEARN and the Assessment of Learning with Information............... 6.1 Evolving Views of Assessment........................................................... 6.2 Looking Ahead.................................................................................... 6.3 Assessment and Learning with Information.......................................

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6.4 I-LEARN and Assessing Learning with Information: Formal Environments.......................................................................... 6.4.1 A Curriculum for Learning with Information?....................... 6.5 I-LEARN and Assessing Learning with Information: Informal Environments....................................................................... 6.6 Conclusion.......................................................................................... References....................................................................................................

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Author Index.................................................................................................... 135 Subject Index.................................................................................................... 137

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List of Figures

Fig. 1.1 Fig. 1.2

The knowledge dimension............................................................... The cognitive process dimension....................................................

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Fig. 5.1 The I-LEARN model....................................................................... Fig. 5.2 Information literacy and I-LEARN................................................. Fig. 5.3 The knowledge dimension............................................................... Fig. 5.4 Information literacy and the knowledge dimension........................ Fig. 5.5 The cognitive process dimension.................................................... Fig. 5.6 Information literacy and the cognitive process dimension.............. Fig. 5.7 I-LEARN and Anderson and Krathwohl’s (2001) Taxonomy......... Fig. 5.8 I-LEARN stages and elements........................................................ Fig. 5.9 Stage 1: Identify.............................................................................. Fig. 5.10 Stage 2: Locate................................................................................ Fig. 5.11 Stage 3: Evaluate............................................................................. Fig. 5.12 Stage 4: Apply................................................................................. Fig. 5.13 Stage 5: Reflect................................................................................ Fig. 5.14 Stage 6: kNow.................................................................................

88 88 91 92 94 96 96 97 98 100 102 105 110 112

Fig. 6.1 I-LEARN stages and elements........................................................ Fig. 6.2 I-LEARN assessment rubric............................................................ Fig. 6.3 I-LEARN and formal instruction: A seventh-grade social-studies activity...................................................................... Fig. 6.4 I-LEARN and informal learning: A trip to a museum.................... Fig. 6.5 I-LEARN and informal learning: Learning with the World Wide Web...............................................................

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Chapter 1

Information as a Tool for Learning

Abstract This chapter sets the stage for the rest of the book by providing an overview of theories from information studies and instructional design and development that suggest the key role of information in learning. Reviewing definitions of information from both fields, the chapter shows how the views converge to present information as a network of entities and relationships that is dynamic, complex, multifaceted, and multipurpose. At its core, information consists of facts, concepts, procedures, and metacognitive strategies—the very things that constitute what we learn. Through learning, information is transferred from the environment into human cognitive systems to become the components of our internal knowledge. Drawing on contemporary understandings of learning as an active, self-directed, internal process by which humans make sense of the information we encounter, the chapter explores the parallels between today’s definitions of information and of learning to argue that information itself is our basic tool for authentic learning in the information age and that accessing, evaluating, and using information are the heart of twenty-first-century skills. Over a hundred years ago, the philosopher William James described the infant’s view of the world as a “big, blooming, buzzing confusion” that enveloped his or her mind (1890, p. 488). If he were writing today, James might conclude that information is the “buzzing confusion” that seems to suffuse our every waking moment. In fact, many authors have provided colorful interpretations of “information”: we’ve all heard that “information is power,” and David McCandless (2010) tells us that “information is beautiful”—a view also promoted by the website of the same name (www.informationisbeautiful.net). President Ronald Reagan once referred to information as “the oxygen of the modern age” that “seeps through the walls topped by barbed wire [and] wafts across the electrified borders” (London Guardian 6/14/89). T.S. Eliot, musing on behalf of many humanists facing the modern age, offered perhaps the most famous questions of all about the nature and role of information: “Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?” (1934, p. 96).

D. Neuman, Learning in Information-Rich Environments: I-LEARN and the Construction of Knowledge in the 21st Century, DOI 10.1007/978-1-4419-0579-6_1, © Springer Science+Business Media, LLC 2011

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1 Information as a Tool for Learning

Every scholarly and professional field defines “information” in ways that connote its own particular needs and foci. For medicine, information includes vital signs, data on drug interactions, and test results; for journalism, it consists (at least in part) of leads, leaks, and recovered emails. For information professionals—librarians, information scientists, information managers, and others who work with various kinds of information in a range of contexts—and for educators—teachers, curriculum developers, instructional designers, library media specialists, and others who work with information within various learning environments—information also has specialized connotations. This book takes the position that information is not only powerful and beautiful but that it is the basic building block for human learning. Drawing primarily on research and practice in the fields of information studies and instructional design and development, the book suggests a way to think about constructing knowledge that is directly applicable in the twenty-first century’s “information age.” It offers ideas that will be of interest to researchers and theorists from its two core disciplines and related fields and also to those who teach the research process—postsecondary faculty, librarians, and information specialists as well as K-12 teachers and library media specialists. In sum, this book is intended for anyone who believes—or who at least wants to consider—the proposition that “developing expertise in accessing, evaluating, and using information is in fact the authentic learning that modern education seeks to promote” (American Association of School Librarians and Association for Educational Communications and Technology 1998, p. 2). Looking at information as it is understood by information professionals and by those who design and deliver instruction leads to a powerful insight: in today’s world, information is, at bottom, the basis for learning. Understanding the nature and role of information in learning is crucial to understanding how learning itself has changed in the information age. Recognizing the profundity of this change is, in turn, critical to fostering deep and meaningful learning in the information-rich environments of the twenty-first century. The perspectives reviewed and offered here provide key information—yes, information—about this phenomenon.

1.1

What Is Information? The View from Information Studies

Traditionally, information theorists have looked at information—and particularly “recorded information”—as their particular focus along a four-stage continuum: data, information, knowledge, and wisdom. In this view, “data” are discrete bits of content that exist independently of one another. “Information” (especially recorded information) implies not only content but some level of organization of that content that integrates its various components. “Knowledge” adds value—and the human dimension—to the continuum by implying cognitive processes that expand basic organizational patterns into more complex understandings that bring various sets of information together. “Wisdom” is the ultimate value-added stage of the continuum, suggesting human understanding and use of organized knowledge with judgment and insight.

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Today’s information professionals are the beneficiaries of Buckland’s (1991) more nuanced definition of information, one that blurs the distinction between information and knowledge and posits that information is more dynamic than such a clear dichotomy suggests. According to Buckland, information can be conceptualized as a process (i.e., the communication act); as knowledge (i.e., an increase of understanding or a reduction in uncertainty); and as a thing (i.e., an object that imparts information). Marchionini (1995) builds on Buckland’s ideas to note that information “is anything that can change a person’s knowledge” and that it “includes objects in the world, what is transferred from people or objects to a person’s cognitive system, and … the components of internal knowledge in people’s minds” (p. 5). Other “theorists of information” offer variations on these definitions that flesh out specific components according to the particular focus of the definer. Scholars concerned with creating information systems, for example, assume that information is something that must be organized according to specific approaches in order to allow efficient access and retrieval—the central concern of librarians and other information practitioners (see, for example, Soergel 1985; Taylor 1999). Researchers who have studied information users’ interactions with such systems have developed process-oriented models of information seeking that assume information is part of a dynamic whole that changes and develops as the information-seeking process progresses (see, for example, Dervin 1983, 1992, 1998; Dervin et al. 2003; Dervin and Nilan 1986; Johnson 2003; Kuhlthau 1985, 1988, 1993, 1997; Pettigrew et al. 2001; Spink 1996; Vakkari and Hakala 2000). Other writers have their own variations on these themes, and the precise definition of “information” continues to be a topic of debate within the field. Overall, however, all the definitions of information within the information field suggest that information is neither a monolithic concept (e.g., the undifferentiated product of “the media”) nor a collection of unrelated pieces (e.g., facts, numbers, images). Rather, it is a series of discrete yet interrelated elements that appear along a continuum ranging from the purely physical to the fully abstract. Both the elements and the interrelationships are constituents of the larger construct of “information.” Content and process as well as external and internal are linked in a complex and dynamic whole. Thinking of information as a complex and multifaceted concept allows us to see it as represented by “entities” and “relationships” that we can mix and match according to their nature and the uses to which we would like to put them. For example, we can conceive of a blog as information in each of Marchionini’s (1995) three senses: it is an object in the world; its content is a particular representation of ideas that is transferred to its readers; and the readers’ internalization of those representations is the “stuff” of their knowledge. An information user might focus on the technological format of the object, the nature and quality of the content to be transferred, or the mechanisms by which one processes and organizes the content to increase understanding or reduce uncertainty. All these foci are information, and all are related to one another in both obvious and subtle ways.

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hat Is Information? The View from Instructional W Design and Development

Within the overarching field of education, the subfield of instructional design and development is the source of most of the theory underlying the conceptualization and creation of learning activities. Also known as “instructional technology” and “educational technology,” this area has been a formal discipline for approximately sixty years and has been defined as “an organized procedure that includes the steps of analyzing, designing, developing, implementing, and evaluating instruction” (Seels and Richey 1994, p. 31). As the definition suggests, the central “information” concern of instructional designers involves selecting, organizing, and presenting information in ways that enhance the possibility of learning. Instructional designers—the usual title for practitioners in the field—are more concerned with the pedagogical uses of information than with organizing information for access and retrieval. But writings from this field echo information specialists’ understanding of information as a set of entities that are discrete and have specific characteristics and relationships. Early—and key—theorists like Robert Gagne (1965, 1985) and David Merrill (1983, 1999) proposed “categories of learning” and “components of learning” that correspond closely to different types of information and of information use, from making simple stimulus-response connections to engaging in complex problem solving. While the details of their work—and the work of many others over the years—need not concern us here, some illustration of the “pieces” of information these two theorists posited provides a useful context. After a lifetime of work on classifying kinds of learning and looking for ways to achieve each kind, Gagne (1985) ultimately proposed five types of “learned capabilities”—verbal information, motor skills, attitudes, cognitive strategies—and four kinds of “intellectual skills”—discriminations, concepts, rules, and problem solving. Focusing on those categories most closely allied with the cognitive dimension implied by the definitions of “information” above, we can see that Gagne’s hierarchy assumes a number of more or less clearly defined subcategories, or types, of information: • Verbal information might be called information at face value, since it consists of symbols such as words or musical notations without reference to their underlying meanings. • Cognitive strategies are techniques and skills—all of which involve knowledge of types of information—that individuals use to manage their learning. • Discriminations involve differences among objects varying in such basic properties as color, shape, size, etc. • Concepts can be concrete (e.g., table) or defined (e.g., democracy) and are in essence ideas about things that are joined by particular relationships into basic categories. • Rules are statements that relate classes of stimuli to classes of responses (e.g., two pints make a quart) that enable us to respond predictably to situations even when we are unable to state an appropriate rule. Gagne considered rules the “stuff of thinking” (Gagne 1985, p. 157). • Problem solving—the category in which a specific kind of information merges inseparably with information use—involves “discover[ing] a combination of

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previously learned rules which can be applied to achieve a solution for a novel situation” (Gagne 1985, p. 155). The elements of discovery, combination, and novelty move this kind of thinking with rules to a higher kind of knowledge. Merrill’s (1983) “component display theory” provides another example of the notion that information consists of discrete but interrelated entities that have particular uses. Merrill proposes that information to be learned consists of four types—facts, concepts, principles, and procedures. He further posits that learning involves three different kinds of performance—remember, use, and find. According to Ragan and Smith (2004), Merrill formed the rationale for his categorization on “some assumptions about the nature of subject matter” (Merrill 1983, p. 298, quoted in Ragan and Smith, p. 632)—suggesting, once again, that theorists of instructional design and development view information as consisting of interrelated entities. Merrill expanded the number and breadth of those entities in his later work by identifying thirteen types of learning in his “instructional transaction theory” (Merrill 1999; Merrill et al.1992). His latest thinking on the topic reaffirms his early work and its proposition that information consists of multifaceted and interrelated components.

1.2.1 The Knowledge Dimension These early ideas are revisited and reflected in a key contemporary view of information from the perspective of instructional development and design: “the knowledge dimension” outlined in Anderson and Krathwohl’s (2001) A Taxonomy for Learning, Teaching, and Assessing. This dimension posits that knowledge—or, in other words, information, as defined above—can be characterized as falling into four categories: factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge. What is significant about this formulation for a discussion of information and learning is that it appears in the current version of “Bloom’s Taxonomy,” one of the most important and widely used sets of ideas in instructional design and indeed in American education for over fifty years. Bloom’s original Taxonomy of Educational Objectives, published in 1956, delineated six “levels of learning” but did not directly specify the types of information involved in these levels. The inclusion of a “knowledge dimension” in this first-ever revision and update of Bloom’s Taxonomy indicates the importance to contemporary instructional design and development of understanding the components of information that underlie learning across the spectrum of levels of complexity. As shown in Fig. 1.1, Anderson and Krathwohl (2001) define four “types of knowledge”: factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge. Within each type of knowledge, the authors identify a number of subtypes: knowledge of terminology, for example, is a subtype of factual knowledge, while strategic knowledge is a subtype of metacognitive knowledge. Examples of each subtype provide even further clarification of the discrete chunks within the subtype: knowledge of the alphabet, for example, is a kind of factual knowledge, while knowledge of planning strategies is a kind of metacognitive knowledge.

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Fig. 1.1 The knowledge dimension

Altogether, “the knowledge dimension” of the Taxonomy comprises four types of knowledge, eleven subtypes, and over sixty examples (or sub-subtypes)—a full array of types of information that are both discrete in their specific content and interrelated through the connections of their hierarchy. The array bears a striking resemblance to the hierarchies devised by information scientists such as Soergel (1985) that lay out categories and relationships of particular subjects as a basis for designing information-retrieval systems. The Internet Public Library is an ongoing example of a system organized according to such a hierarchy (www.ipl2.org).

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1.2.2 The Cognitive Process Dimension Fig. 1.2 displays Anderson and Krathwohl’s (2001) “cognitive process dimension.” This dimension—a revision of the “levels of learning” that comprised Bloom’s (1956) original Taxonomy—lays out six categories of learning arranged in a hierarchy

Fig. 1.2 The cognitive process dimension

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Fig. 1.2 (continued)

based on complexity: remember, understand, apply, analyze, evaluate, and create. Each of these categories also includes its own subcategories—nineteen in all—that further delineate the chunks within the categories themselves: classifying is a subcategory of understand, for example, while critiquing is a subcategory of evaluate. Like the taxonomy provided for kinds of knowledge, the one provided for categories of learning mirrors similar work in information science. To varying degrees, the different types of knowledge support different kinds of processing, but this relationship is obviously flexible: both factual knowledge and

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metacognitive knowledge can support all six levels, for example, although each is more likely than others to come into play at various levels. The existence of this web of relationships reflects the connections between content and process, complexity and dynamism, that are characteristic of conceptions of information held by the instructional-design field in general.

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The Views Converge

The conceptions of information presented above suggest that the fields of information science and instructional design and development consider information in much the same way. Anderson and Krathwohl’s (2001) Taxonomy maps closely to Buckland’s (1991) process-knowledge-thing characterization of information and to Marchionini’s (1995) object-representation-knowledge typology. Both the “information-science” and the “instructional-design-and-development” viewpoints present information as a holistic construct that incorporates discrete content entities linked by process-related dimensions. Both suggest that information has both physical and abstract qualities that can be put to different uses depending on their inherent characteristics and their possible interrelationships. Both consider the border between information and knowledge to be a porous one that is crossed when information types and information uses intersect. Ultimately, both consider “information” as dynamic, complex, multifaceted—and multipurpose. What this means for a discussion of information as a tool for learning is this: although theorists in the different fields use different vocabularies and come from different perspectives, the concepts underlying their work are basically the same. Whether they talk about “information” or “types of knowledge” and “cognitive processes,” they are addressing the “stuff” that humans use to construct and store meaning. “Red means stop” is both a fact and a piece of information; “Annie Leibovitz is the greatest photographer working today” is both an evaluation and a piece of information. Taken together, the two fields offer important insights that, in turn, suggest how twenty-first century learners can make sense of the world around them.

1.4

What Is Learning?

What are the implications for learning that flow from instructional designers’ and information scientists’ shared understanding of information? The question is key, of course, but it cannot be answered without consideration of the nature and processes of learning itself. Theorists have pondered these phenomena for centuries, although the “scientific” study of learning began only about a hundred years ago. Today, as a result of extensive research into what has been called “cognitive information processing,” our understanding of learning is as dynamic, complex, and multifaceted as our understanding of information itself.

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1.4.1 Early Learning Theory Early twentieth-century psychologists worked in the behaviorist tradition, arguing that the proper focus for scientific experiments was only that which could be observed. Therefore, only behaviors that preceded and followed mental processes— and not the processes themselves—could be studied empirically. For a good part of the twentieth century, the study of learning involved studying external elements of learning—stimuli, responses, and processes and activities that would reinforce appropriate connections among them. Behaviorists conceived of learning as a relatively permanent change in behavior or the ability to behave and left the study of the contents and workings of the “black box” to future researchers. At about the same time, Piaget and other developmental theorists, working along a different track, added a new dimension to the understanding of human learning. Piaget’s (1952) theory of stages of cognitive development—from the “sensorimotor” stage of the youngest children to the “formal operational” stage that characterizes adult thinking—proposed that internal abilities and structures influenced what and how individuals process information. These abilities and structures expand and become increasingly complex with age, allowing us to learn more advanced and abstract concepts and strategies as we mature. With ideas that foreshadowed those of contemporary learning theorists, Piaget focused on internal mechanisms that learners use to integrate new information with preexisting knowledge to create new understandings. Quite possibly the “first constructivist,” Piaget wedded internal information-processing functions with external information objects in a holistic view of learning.

1.4.2 Learning Theory Today That holistic view prevails today, when contemporary learning theorists study not only developmental influences on learning but social, cultural, psychological, and biochemical influences as well. In the National Research Council’s influential report entitled How People Learn, Bransford et al. (2000) include all these areas in their definition of the field now called “cognitive science”—an approach to the study of learning “from a multidisciplinary perspective that include[s] anthropology, linguistics, philosophy, developmental psychology, computer science, neuroscience, and several branches of psychology” (p. 8). Delving deeply into the black box that the behaviorists declined to examine, today’s learning theorists describe learning as an active, personalized, self-directed internal process by which human beings make sense of the world: “In the most general sense, the contemporary view of learning is that people construct new knowledge and understandings based on what they already know and believe” (p. 10). Cognitive science encompasses views of learning as an outcome as well as a process. It generally assumes the existence of facts, concepts, procedures, and

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strategies—the categories noted by Anderson and Krathwohl (2001) above—but is more concerned with how the mind represents and structures these types of information in long-term memory than with the nature of the information itself. Whether called “schemata” or “mental models,” these structures are organized collections of information that exist at a higher level of abstraction than immediate experience, that are dynamic and changeable as a result of experience or instruction, and that provide a context for interpreting new knowledge (Winn 2004). Although theorists still quarrel about specific distinctions between these two kinds of structures, they agree on the central point that “learning” exists in our minds as an interconnected, multifaceted, dynamic, and complex web of information. Contemporary learning theory marries dimensions of process and content to create an overall picture of how individuals acquire, understand, and use information. Drawing on theories and research from the full spectrum of associated disciplines, cognitive scientists work to discover how people “learn with understanding” rather than simply making stimulus-response associations or retrieving “mere list[s] of disconnected facts” (Bransford et al. 2000, pp. 8–9). Cognitive scientists define the process of learning as a highly individualized and complex set of activities that involves the active construction of personal understandings of information that can be put to relevant use. They see the results of this process—the state of learning—as a rich and multidimensional collection of content, process, and strategic knowledge that is unique to each individual.

1.5

Information and Learning

“Learning”—like “information”—consists of multifaceted and interrelated elements that exist in some kind of organized cognitive structures within individuals. While definitions of information only allude to the processes by which this organizing takes place, definitions of learning focus primarily on these processes. This focus on process—acquisition, short-term memory functions, metacognitive strategies, long-term memory components, and strategies for retrieving and communicating what has been learned—complements the focus on “information” that both information scientists and instructional developers and designers share. In fact, contemporary definitions of information and of learning echo each other in both form and substance. One can easily define “learning” with terms from Marchionini’s (1995) definition of information: learning consists of creating structures through encounters with “objects in the world,” transferring them to personal “cognitive system[s],” and forging them into “the components of internal knowledge” (p. 5). This understanding of learning carries a wealth of implications for all of us— and especially for teachers, instructional designers, librarians, library media specialists, and other educators charged with helping students become effective learners who can flourish in the information age. Above all, it suggests that learning is about building structures based on information. Every level of learning in the Anderson and Krathwohl (2001) Taxonomy is supported to varying degrees by

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different types of information: it is obvious, for example, that one remembers facts, understands concepts, and applies procedures. Even at each of these three “lower” levels, there is often a place for all the other kinds of information as well. And at the “higher” levels—analyze, evaluate, and create—it is clear that facts, concepts, procedures, and metacognition all come into play in each instance of the type of learning attempted. Clearly, the work of the instructional theorists discussed above is built on assumptions about the role of information in learning. The connection between “the information” and “the learning,” however, has rarely, if ever, been made explicit. In fact, until recently only limited work has been done specifically to bring together concepts from “information” and those from “learning”: in 1993, Eisenberg and Small reported that “[s]erious research into the relationships between various information attributes and education is limited, narrow, haphazard, and unconnected, at best” (p. 263). Neuman (1993, 1995) addressed the relationship between information and learning in a series of studies, and Hill and Hannafin (2001) also brought related ideas to the fore. Still, direct attention to the relationship between learning and information has advanced only marginally since Eisenberg and Small’s 1993 lament—which was published before the Internet, the World Wide Web, and other technological advances made information ubiquitous and its role in society a constant theme and a constant challenge. Only one academic journal—Education and Information Technologies, which began publication in 1996—is devoted to the topic. Mayer’s (1999) instructional-design model is actually information-based, although he doesn’t make the connection explicit. More recently, Kuhlthau et al.’s (2007) promotion of “guided inquiry” assumes that information is at the heart of learning, and Hannafin and Hill’s (2008) decision to revisit the field of “resourcebased learning” because “information has changed dramatically during the past 25 years” (p. 525) suggests that information-based learning is making an appearance in the literature of instructional design and development as well. Finally, Ford’s (2008) recent announcement of a field he calls “educational informatics” also suggests that the time is right for increasing attention to this important area. So far, explicit theoretical grounding for working across the disciplines of information studies and instructional design and development has come largely from the world of information studies. For example, the comprehensive work of communications theorist Brenda Dervin (1983, 1986, 1992, 2003) has had a strong influence on the field of information science and has long been cited as a conceptual bridge between information seeking and the ways in which people actually engage with the information they seek and find. Her “sense-making” methods and her emphasis on closing the “cognitive gap” to make sense of observed data led many information researchers to look to relevant cognitive issues. Offering “a set of metatheoretic assumptions and propositions about the nature of information, the nature of human use of information, and the nature of human communication” (Dervin 1992, pp. 61–62), Dervin might be said to have set the stage for a consideration of information as a tool for learning. Similarly, Kuhlthau’s (1985, 1988, 1993, 1997) work on the Information Search Process laid important groundwork for looking at information-seeking within particular learning situations—elementary, secondary,

1.6 Conclusion

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post-secondary, and professional environments. Together, these two theorists’ focus on information users’ needs and behaviors undergirds much current work in the information field and is clearly related to learning as well. T. D. Wilson (1981, 1999) follows a slightly different track but also suggests a bridge between the information professions and the instructional ones. His model of “information behavior” embeds information seeking within a broader context and extends the purview of the information field to include what might be done with information after it has been found. His model’s inclusion of a step entitled “information processing and use”—a step generally not found in informationseeking models—invites students of information, and not just researchers in end users’ disciplines, to investigate ways in which information is actually used after it has been found. It seems clear that now is the time to consider the role of information in learning in a direct and comprehensive way and that merging ideas from information science and instructional design and development yields a compelling framework for that consideration. Focusing on learning as the construction of meaning from information implies a synthetic process in which the learner accesses, evaluates, and uses pieces of information to generate new and personally meaningful understandings of the world. Knowing what kind of information is needed, how to find and retrieve that information, how to understand and evaluate its quality and applicability, and how to put it into a coherent structure is the essence of twenty-first-century learning. Looking at information and learning as complementary elements can provide important and useful guidelines for helping all of us become more effective learners in the information age.

1.6

Conclusion

Information—as seen by theorists and researchers from both information studies and instructional design and development—is far from a monolithic concept. Rather, contemporary thought in both fields holds that “information” is a complex, multifaceted, and holistic construct that incorporates (1) specific components that are (2) linked by various kinds of processes and relationships that are forged by a variety of contextual factors. In this construct, information has both physical and abstract dimensions that can be put to different uses depending on their inherent characteristics and possible interrelationships. One of those uses is, of course, learning. Learning, too, is complex and multifaceted. And because of this, scholars from various perspectives tend to focus on particular components of it rather than on the overarching concept itself. Essentially, however, the contemporary understanding of learning is that it is the construction of personal meaning from the facts, concepts, rules, and procedures that comprise information. It is both the process and the outcome of (1) acquiring new concepts and skills through instruction or experience and (2) organizing those concepts and skills into personally coherent structures within our minds.

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Thus learning—like information—consists of multifaceted and interrelated elements that exist in some kind of organized structure. And while definitions of information only allude to the processes by which this organizing takes place, definitions of learning are concerned primarily with those processes and how they “work on” various kinds of knowledge/information: what the sensory register, short- and longterm memory, metacognitive strategies, and so forth contribute to the creation of organized cognitive structures. Learning and information are two sides of the same coin that complement each other in unique ways. Each side of that coin represents a dynamic, complex, and multifaceted reality. As a whole, the coin indicates that “information” and “learning” are inseparable and that information is the basis for learning in today’s dynamic, complex, and multifaceted world. In the information-rich environments in which we live our daily lives, information and learning converge in our efforts to understand those lives. Information, then, is the basic building block of learning— the “stuff” we access, evaluate, and use to make sense of our world.

References American Association of School Librarians and Association for Educational Communications and Technology (1998). Information power: Building partnerships for learning. Chicago: ALA Editions. Anderson, L.W., & Krathwohl, D. R. (Eds.), (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s Taxonomy of Educational Objectives. New York: Addison Wesley Longman. Bloom, B. S. (Ed). (1956). Taxonomy of educational objectives: Cognitive domain. New York: Longman. Bransford, J.D., Brown, A. L., & Cocking, R. R. (Eds.), (2000). How people learn: Brain, mind experience, and school. Washington, DC: National Academy Press. Buckland, M. (1991). Information and information systems. New York: Praeger. Dervin, B. (1983, May). An overview of sense-making research: Concepts, methods, and results to date. Paper presented at the meeting of the International Communication Association, Dallas, TX. Dervin, B. (1992). From the mind’s eye of the user: The sense-making qualitative-quantitative methodology. In J. Glazier & R. Powell (Eds.), Qualitative research in information management. (pp. 61–84). Englewood, CO: Libraries Unlimited. Dervin, B. (1998). Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of Knowledge Management, 2(2), 36–46. Dervin, B., Foreman-Wernet, L., & Lauterbach, E. (Eds.), (2003). Sense-making methodology reader. Cresskill, NJ: Hampton Press. Dervin, B., & Nilan, M. (1986). Information needs and uses. Annual review of information science and technology, 21, 3–33. Eisenberg, M. B., & Small, R. V. (1993). Information-based education: An investigation of the nature and role of information attributes in education. Information Processing & Management, 29(2), 263–275. Eliot, T. S. (1962). Choruses from “The Rock.” In Collected poems 1909–1935. New York: Harcourt. Ford, N. (2008). Educational informatics. Annual review of information science and technology 42, 497–546.

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Gagne, R. M. (1965). The conditions of learning. New York: Holt, Rinehart, and Winston. Gagne, R. M. (1985). The conditions of learning (3rd ed.). New York: Holt, Rinehart, and Winston. Hannafin, M. J., & Hill, J. R. (2008). Resource-based learning. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll, M. P. (Eds.), Handbook of research on educational communications and technology (3rd ed.). (pp. 525–536). Mahwah, NJ: Lawrence Erlbaum. Hill, J. R., & Hannafin, M. J. (2001). Teaching and learning in digital environments: The resurgence of resource-based learning. Educational Technology Research and Development, 49(3), 37–52. James, W. (1890). The principles of psychology (Vol. I). (p. 488). Henry Holt. Johnson, J. D. (2003). On contexts of information seeking. Information Processing and Management, 39(5), 735–760. Kuhlthau, C. C. (1985). A process approach to library skills instruction. School Library Media Quarterly, 13(1), 35–40. Kuhlthau, C. C. (1988). Longitudinal case studies of the Information Search Process of users in libraries. Library and Information Science Research, 10(3), 257–304. Kuhlthau, C. C. (1993). Seeking meaning: A process approach to library and information services. Norwood, N.J.: Ablex. Kuhlthau, C. C. (1997). Learning in digital libraries: An Information Search Process approach. Library Trends, 45(4) 708–725. Kuhlthau, C. C., Maniotes, L. K., & Caspari, A. K. (2007). Guided inquiry: Learning in the 21st century. Westport, CN: Libraries Unlimited. Marchionini, G. (1995). Information seeking in electronic environments. Cambridge, MA: Cambridge University Press. Mayer, R. (1999). Designing instruction for constructivist learning. In C. M. Reigeluth (Ed.), Instructional design—Theories and models. Vol. II: A new paradigm of instructional theory. (pp.141–159). Mahwah, NJ: Lawrence Erlbaum Associates. McCandless, D. (2010). Information is beautiful. London: Harper Collins. Merrill, M. D. (1983). Component display theory. In C. M. Reigeluth (Ed.), Instructional design— Theories and models. (pp. 279–333). Mahwah, NJ: Lawrence Erlbaum Associates. Merrill, M. D. (1999). Instructional transaction theory: Instructional design based on knowledge objects. In C. M. Reigeluth (Ed.), Instructional design—Theories and models. Vol. II: A new paradigm of instructional theory. (pp. 397–424). Mahwah, NJ: Lawrence Erlbaum Associates. Merrill, M. D., Jones, M. K., & Li, Z. (1992). Instructional transaction theory: Classes of transactions. Educational Technology, 32(6), 12–26. Neuman, D. (1993). Designing databases as tools for higher-level learning: Insights from instructional systems design. Educational Technology Research and Development, 41(4), 25–46. Neuman, D. (1995). High school students’ use of databases: Results of a national Delphi study. Journal of the American Society for Information Science, 46(4), 284–298. Pettigrew, K. E., Fidel, R., & Bruce, H. (2001). Conceptual frameworks in information behavior. Annual review of information science and technology, 35, 43–78. Piaget, J. (1952). The origins of intelligence in children. New York: International Universities Press. Ragan, T. J., & Smith, P. L. (2004). Conditions theory and models for designing instruction. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 623–649). Mahwah, NJ: Lawrence Erlbaum. Reagan, R. (June 14, 1989). The London Guardian, p. 24. Seels, B., & Richey, R. (1994). Instructional technology: The definitions and domains of the field. Washington, DC: Association for Educational Communications and Technology. Soergel, D. (1985). Organizing information: Principles of data base retrieval systems. Orlando: Academic Press. Spink, A. (1996). Multiple search sessions model of end-user behavior: An exploratory study. Journal of the American Society for Information Science, 47(8), 603–609.

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Taylor, A. G. (1999). The organization of information. Englewood, CO: Libraries Unlimited. Vakkari, P., & Hakala, N. (2000). Changes in relevance criteria and problem stages in task performance. Journal of Documentation, 56(5), 540–562. Wilson, T. D. (1981). On user studies and information needs. Journal of Documentation, 37, 3–15. Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55, 2249–270. Winn, W. (2004). Cognitive perspectives in psychology. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 79–112). Mahwah, NJ: Erlbaum.

Chapter 2

Information-Rich Environments: Single-Sense, Multisensory, and Interactive

Abstract This chapter defines information-rich environments, explains the range of information objects that constitute such environments, and outlines the learning affordances these objects offer as identified by decades of research in environments that largely predate the Internet and the World Wide Web. Although research interest in such “traditional” environments has waned in recent years, understanding how the characteristics of various information formats can support learning in their own unique ways is prerequisite to exploiting the full learning potential of today’s information-rich environments. The chapter surveys the learning affordances of single-sense, multisensory, and stand-alone interactive information formats both to explore how these formats can support learning in their own right and to provide a foundation for considering how they can support learning in the more complex and interconnected venues available today. Concluding with a focus on interactivity— the primary learning affordance of the twenty-first century’s most compelling learning environments—the chapter ties information to learning across the full range of information-rich environments. In the twelfth century, an information-rich environment was a monastery that supported a scriptorium and a cadre of skilled calligraphers and illuminators. During the Renaissance, it was the private library of a wealthy family that included a copy of Gutenberg’s Bible and other flowerings of that latest technology, the printing press. In the Enlightenment, an information-rich environment was a palace that housed musical scores and great works of art as well as printed and hand-copied poetry, literature, and records—along with resident artists, musicians, and scholars who created, mined, and managed these treasured collections. Today’s information-rich environments resemble that Enlightenment palace far more than the medieval monastery—but without the protective palace walls that isolated it and often without the skilled and knowledgeable courtiers to answer questions about its treasures. In the twenty-first century, in fact, we can no longer speak of separate and discrete information-rich environments as if they were self-contained and self-regulated. The Internet and World Wide Web have transformed isolated information environments (both rich and poor) into a kind of “global information D. Neuman, Learning in Information-Rich Environments: I-LEARN and the Construction of Knowledge in the 21st Century, DOI 10.1007/978-1-4419-0579-6_2, © Springer Science+Business Media, LLC 2011

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2 Information-Rich Environments: Single-Sense, Multisensory, and Interactive

village,” populated by a full range of villagers. While we must discuss individual information-rich environments separately if we are to understand them and help learners work effectively within them, we must also remember that no single information environment exists solely on its own. Learners must master knowledge and skills appropriate to specific environments and to interconnected environments as well.

2.1

What Is an Information-Rich Environment?

An information-rich environment is any venue—formal or informal, actual or virtual—which contains information objects in any format that could be used for learning. Today’s information-rich environments exist in brick-and-mortar schools, libraries, and museums; “traditional” media outlets like television, radio, and even gaming devices; and, of course, the Internet and the World Wide Web. Informationrich environments can be found in formal educational settings, like classrooms and laboratories, and in informal educational settings, like the neighborhood branch of the local public library and the art museum in the center of the city. They can bypass educational settings entirely, offering possibilities for learning disguised as recreation and entertainment—a movie theater showing a commercial film about the struggle against apartheid, a radio station broadcasting a talk show about a hot political issue, a game box housing an adventure in which a player must use coordinates to navigate through space to land on an asteroid. The information-rich environment that captures most of our attention today, of course, is centered on the Internet and the World Wide Web. With seemingly inexhaustible text, sound, and still and moving visual resources, the Internet / Web venue offers a wealth of “raw material” for learning. With its ability to respond instantaneously to each individual’s question, to provide feedback just as quickly to each individual’s answer, and to host individuals’ creation of their own content, it supports the full spectrum of cognitive processes required to take full advantage of the potential of information as a learning tool. Gathering many discrete information-rich environments under its vast umbrella, the Internet / Web provides an evermore-compelling information-rich environment. Learners must develop a wide range of concepts, attitudes, and skills to live happily and productively in that environment throughout their lives.

2.2

Information Objects in Information-Rich Environments

Information-rich environments offer a variety of information objects—that is, physical and virtual entities that contain various kinds of information, from mathematical formulas to architectural mock-ups. Moreover, information-rich environments offer these objects in a variety of formats—printed materials, audio presentations, “motion media” like film and video, and digital resources that range from drill-and-practice

2.2 Information Objects in Information-Rich Environments

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programs to relational databases to blogs and wikis to social-media sites. The richest information environments include objects from the full range of information formats. Thus, they require learners to employ the full scope of concepts and skills— from remember to create (Anderson and Krathwohl 2001)—as they interact with a variety of information objects and transfer various types of content representation into internalized knowledge (Marchionini 1995). In such environments, the interplay of information and learning is perhaps easiest to see. To get that full picture, we must understand the learning processes and requirements that each information format accommodates. Viewed in terms of learning affordances, information formats can be categorized not only according to the type of information they present (e.g., visual or verbal) but—more importantly, noted Robert Kozma (1991)—according to the kinds and levels of cognitive engagement they support. Kozma’s (1991) typology of media according to their “cognitively relevant characteristics” revolutionized the study of instructional media and provides a key conceptual framework for discussing learning with information. To create this typology, he reanalyzed decades of “media research” from a constructivist perspective (rather than a behaviorist one) and concluded that media are tools that help learners create knowledge rather than mere “delivery trucks” (Clark 1983) that only distribute content and have nothing to do with the processes or outcomes of learning. Arguing that we learn with media through an active, constructive process (rather than from media through a passive, receptive one), Kozma (1991) drew on decades of research to buttress his view that different media formats—that is, different representations of information—support different kinds of learning. His ideas are mainstream today, but they were startling only twenty years ago. Adapting Kozma’s (1991) scheme, we can categorize information objects into three types—single-sense, multisensory, and interactive. This three-part scheme both addresses the natures of the formats and suggests the kind and degree of cognition linked to each: as explained below, learning with a film or video, for example, demands a somewhat different set of cognitive activities than learning with a still photograph. Individual information objects can occupy more than one category—for example, a database of newspaper articles is “single sense” in that it includes only print but “interactive” in that it allows individualized queries and responses. Similarly, learning from different kinds of information objects often involves overlapping skill sets—in the preceding example, reading for information and navigating a particular interface. Focusing particularly on the primary cognitive skill (or skills) and level (or levels) an object supports, however, allows us to identify the processes learners must tap to learn most efficiently and effectively from that object.

2.2.1 Single-Sense Information Objects Single-sense information objects contain content that is encountered through the application of only one sense, usually sight or hearing. The most common single-sense formats, of course, are the printed and the spoken word. Despite the proliferation

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of other formats, particularly since the mid-1990s, printed and spoken words—in books, articles, lectures, panel discussions, and similar formats—still constitute the primary information objects we use for learning and for other “serious” pursuits. Verbal fluency, then, continues to be the basic skill students must develop to learn in an information-rich environment. 2.2.1.1 Visual information objects Words-on-paper are the most prevalent visual information objects we encounter: textbooks, newspapers, flyers, brochures, and similar printed products consist primarily of words that learners must decode, comprehend, and organize in memory in order to construct personal knowledge. Even in the world of the Internet / Web, words-on-the-screen comprise the bulk of the information we encounter. Reading is thus the key to learning from most visual information objects, and decades of research on reading have uncovered a wealth of detail about the specific learning affordances that the printed word provides. The International Reading Association, for example, notes that its journal Reading Research Quarterly has been “essential reading for those committed to scholarship on literacy for learners of all ages” for forty years (http://www.reading.org/). Currently, both research and popular attention to reading online have added a new twist to issues of “print” literacy: “Online, R U Really Reading?” asked Mokoto Rich in the New York Times in the summer of 2008. Drawing from media research rather than reading research, Kozma (1991) provides another view of the learning affordances of print. He notes that print (and, in particular, the book) is a stable, static format that allows a learner to set his or her own pace, to pause over content, to reread and ponder difficult or unfamiliar information, and to focus intently on specific details. When printed materials include pictures as well as words, the learning affordances are magnified: pictures help students to recall information they’ve previously learned, to clarify new information, and to create two-dimensional mental models of phenomena that have visual as well as verbal content. As multimedia expert Richard Mayer famously wrote over a decade later, “[P]eople learn more deeply from words and pictures than from words alone” (Mayer 2005, p. 31). Consider, for example, the learner studying the American Revolution through a traditional textbook. He or she determines how quickly to read the text—perhaps skimming over familiar information about the early colonies but pausing over the names of unfamiliar generals and battles. Coming to terms with the complex political and economic drivers of the Revolution would take more effort: rereading sections and concentrating carefully to understand the connections between the Stamp Act and political unrest. Focusing on details of specific places and events—like an extended description of the original Boston Tea Party—could add immediacy to the learner’s broader theoretical view. And looking at pictures—a drawing of the familiar design of Betsy Ross’s flag, a timeline that clarifies the progression of events from “the shot heard ‘round the world” to the proclamation of the Bill of Rights, a map


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that provides a schematic of Paul Revere’s ride—could enrich students’ understanding as well as their ability to remember the information. The learner’s textbook—a printed information object—provides a rich environment for this kind of learning. Ultimately, Kozma (1991) concluded that, as a format, print supports learning that requires serial (rather than simultaneous) processing, close study, and the careful integration of details. The stability of the format—particularly in contrast with the dynamism of video or electronic media—supports learning that depends on prolonged attention to specific elements, the development of complex yet static mental models, and the construction of deep understandings that are linquistically based. Information objects in print can support learning that is as straightforward as mastering basic reading skills and as sophisticated as understanding the most challenging ideas in history, literature, and philosophy. While reading calls for linguistic understanding, learning from many printed information objects requires what might be called visual understanding. According to Smaldino et al. 2008), single-sense formats that call for visual learning include still pictures (i.e., photographs and slides), drawings, charts, graphs, posters, and cartoons. Each of these has its own characteristics, but as a group these “visual” information objects tend to have similar advantages: they represent ideas in a way that is often easier to understand than verbal descriptions, they simplify information, and they make information more memorable. The pictures in our Revolutionary War example illustrate all these learning affordances. Consider how much easier it is to conceptualize the progression of the Revolution from a timeline than from a list of dates, names, and places. And imagine the complexity—and length—a verbal description of the shapes and colors of Betsy Ross’s flag would require in place of that simple drawing. Both these examples show the memorability of visual representations of information in comparison with verbal depictions, but the map of Revere’s ride provides an especially interesting instance of the power of visual information objects. According to Paivio’s (1986, 1991) dual-trace theory, visual information and verbal information are processed through separate cognitive channels and according to different cognitive processes. Verbal information, whether read or heard, must be encountered and processed sequentially on its way to long-term verbal memory; visual information, in contrast, is perceived simultaneously and as a unified whole and may pass more directly into long-term visual memory. Although visual-memory processing is complex in its own right, it seems to be less compartmentalized than verbal-memory processing. In any event, “because of their concreteness, images are superior to words in promoting recall” (Fletcher and Tobias 2005, p. 119). Thus, a visual internal representation of the map is far more likely to remain prominently in the learner’s memory than a narrative description of Revere’s route. As with the research on reading, the research on visuals and learning is extensive. Anglin et al. (2004) list ninety studies on the role of “static” visuals in knowledge acquisition—and another seventy-eight on the role of “dynamic” visuals in this process. Despite the difficulty of drawing firm conclusions across such a vast

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range of disparate work, the authors offer several broad principles related to how static visuals affect learning—for example, “there is a curvilinear relationship between the degree of realism in illustrations and the subsequent learning that takes place” (p. 876). And while they cite research from as long ago as the 1980s into the specific learning affordances of illustrations, they conclude that “much remains to be done” on the whole question of the influence of static visuals on learning— including research “on how students use or do not use illustrations” as tools for learning (p. 876). Despite the absence of comprehensive knowledge of precisely how single-sense verbal and visual information objects support learning, it is clear that they do so. It is also clear that, in addition to providing the receptive-learning affordances noted above, they support expressions of that learning as well. Creating such information objects—writing scholarly papers and imaginative literature, designing graphs and posters, composing and taking photographs, and engaging in similar activities to construct other kinds of visual and verbal information objects—are all examples of learning at the highest level of Anderson and Krathwohl’s (2001) taxonomy. 2.2.1.2 Auditory information objects Single-sense formats that call for auditory learning in an information-rich environment include lectures, panel discussions, and similar “live” activities as well as audiotapes, compact discs, radio broadcasts, and audio computer files. Live presentations, of course, remain the primary information objects used in many formal learning venues, while the “technological” auditory objects are often relegated to supporting roles. Recordings are mainstays of music and literature classes—allowing students to encounter Ella Fitzgerald singing “How High the Moon,” for example, or to experience Robert Frost reading “Stopping by Woods on a Snowy Evening”— but find few uses in the broader curriculum. One exception to this pattern is the provision of recorded text for students who have visual or learning disabilities or who are learning a new language. For these learners, recorded information generally either supplants or supplements information available in printed form. In informal learning venues, auditory information objects have a more extensive presence: audiotours of exhibits are standard fare in twenty-first century museums, and audiobooks accompany many commuters and travelers on their treks. Talk radio has emerged as a powerful political tool—offering the same range of news, opinion, and analysis pioneered by newspapers in their heyday. Overall, however, auditory information objects (other than lectures) have received little research attention as tools for learning (Barron 2004), and the promise of single-sense auditory technology has yet to be fully exploited. Perhaps the growth of podcasting as an information-delivery format will provide a venue for detailed research on the learning affordances of auditory media. Although we know that sound is a compelling sensory stimulus, we know comparatively little about how to use it to support learning beyond the obvious ways noted above (Bishop et al. 2008). Some things we know intuitively: rhythm,


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a characteristic that is dependent on sound, helps us remember things like the multiplication tables and the ABCs. On a more formal level, we know that one key learning affordance of verbal presentations of information stems from the fact that learners’ receptive vocabularies are higher than their expressive ones. Because a learner can understand more complex spoken verbal information than he or she can read or write, live and recorded auditory information objects can be used to convey information at higher conceptual levels than print can convey to the same audience. Auditory information objects, then, can be useful for boosting learners’ conceptual understanding—especially of facts, concepts, and procedures that they have not yet mastered but that are within their “zone[s] of proximal development” (Vygotsky 1978). As with single-sense visual information objects, auditory information objects have expressive-learning affordances as well as receptive-learning ones. Constructing and presenting such objects—podcasts of verbal explanations and arguments, tapes of oral histories, CDs of music performances and compositions—all exemplify learning at the “create” level of Anderson and Krathwohl’s (2001) taxonomy. These activities, like their visual counterparts, call on learners to use the taxonomy’s full range of concepts and skills—from remember to create—as they manipulate information to represent their internalized knowledge (Marchionini 1995) in ways that communicate that knowledge to others. Even in relation to these “simplest” of media formats, learners can become deeply active participants in information-rich environments as they help to forge those environments themselves.

2.2.2 Multisensory Information Objects Multisensory information objects are those whose content is encountered through the application of at least two senses. The senses involved are primarily sight and hearing, although the other three senses can also come into play: touch is a frequent route to information, especially for young learners and those with visual impairments. Even taste and smell can convey information about the nature and composition of “multisensory” information objects: consider the pleasant—and unpleasant—tastes of experimental concoctions in the home or living-skills kitchen; the gentle—and perhaps putrid—scents of flowering plants in the botanical garden; and the arresting stench of formaldehyde in the high school biology lab. 2.2.2.1 Static multisensory information objects Learners tap the information potential of real objects, models, manipulatives, and displays most directly through sight and touch. However, they also extract information about these information objects through hearing about their properties and uses. For example, consider the importance of the “sound track” that accompanies a teacher’s display of a collection of rocks and explains their formation over

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geological time. Similarly, learners construct understandings of complex structures like the internal combustion engine by seeing and touching—and talking and hearing about—mechanical models. Simple manipulatives like movable models of the human body as well as sophisticated displays like museum exhibits, collections of historical costumes, and artifacts of all kinds invite students to make meaning by seeing, touching, speaking, and hearing about the information that is inherent in them. Approaching objects like these as sources of information allows learners to think about how to focus on and extract important aspects of that information (receptive learning) in order to construct meaningful knowledge about it (expressive learning). Someone who wants to understand women’s roles in the American West in the 1840s, for example, would do well to focus on the dresses of the miner’s wife and of the saloon hostess rather than on the picks and shovels in a historical society’s exhibit on the Gold Rush. 2.2.2.2 Dynamic multisensory information objects The word “multisensory” connotes “technological” information objects as well as the hands-on objects noted above: film, video, and television involve both sight and hearing. Such “dynamic” objects also share an additional characteristic—motion— that enhances their potential as information objects. While motion is not a sense like sight and hearing, it allows for the manipulation of time and space so that learning cues can be emphasized. Even Richard Clark (1983), who railed against the view that media enhance learning, pointed out the learning affordances that stem from motion: for example, highlighting key information by animating it and isolating important factors by zooming in on them. Kozma (1991) pointed out that television—then as now the most-researched motion medium—offers additional affordances. It capitalizes on redundancy, presenting complementary information both verbally and visually and requiring learners to process it through different cognitive channels. It creates a “window of cognitive engagement” (p. 189), allowing learners to choose the level at which they process information: effortlessly and shallowly, when the information is general or familiar, and purposefully and deeply, when some cue—usually in the audio portion—alerts the learner that the information is salient. When the window of engagement is fully open and the learner is actively working to internalize ideas, he or she pays close attention to details, elaborates the information more fully in order to remember it, and draws more comprehensive inferences based upon it. One learner watching a video of Hamlet, for example, might engage at a superficial level with visual depictions of setting and costume and at a much more profound level with the verbal sparring that defines the relationship between the Prince and his mother. Another learner might reverse these levels because of strengths in visual learning and a strong interest in the technical aspects of theater. The complexity of an information object in this multisensory format invites learners to call upon the full spectrum of knowledge types and cognitive processes (Anderson and Krathwohl 2001) to construct highly personal meanings. The obvious existence of


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learners who are engaged only minimally—for example, the bored student who focuses only on the play’s few examples of comic relief—reminds us that no information object requires learners to accept that invitation. Perhaps the most cognitively important characteristic of motion media is their dynamism, which allows learners to construct mental models not just of concepts but of processes (Kozma 1991). This learning affordance allows students to understand the emergence of the butterfly from its cocoon, the flow of electricity through a series of circuits, the most effective way to hit a tennis ball. Information objects that incorporate “the magic of motion” can help learners develop sophisticated understandings of nonphysical processes as well: a video that traces the progression of the Battle of Gettysburg over three July days in 1863 can support a learner’s developing understanding of the personal and military factors that rose and fell as an anticipated Confederate victory became a crucial win for the Union. Seels et al. (2004) uncovered over 20,000 citations of articles about television in the ERIC database and over 6,000 in the PsychINFO database. Their massive review of this rich body of research on learning from film and television covers historical, technical, social, theoretical, methodological, and even pop-cultural aspects: does television watching relate to lack of physical activity and therefore to obesity? They also note that research has shown that films can be “effective in teaching inquiry learning and problem solving” as well as “effective in teaching observation skills and attention to detail” (p. 254)—findings that highlight the “information” components of dynamic multimedia information objects and that are consistent with Kozma’s (1991) conclusions as well. Within their comprehensive treatment, the authors also discuss research on specific aspects of motion media that relate to their learning affordances. Referring to these aspects variously as “production effects” and “formal features,” Seels et al. (2004) identify a number of individual attributes of these particular information objects that support learning. They conclude that using such “production effects” as “zooms, cuts, dissolves, and . . . manipulation of program pacing and use of various audio and graphic effects” (p. 259) has become standard practice because of their apparent value in supporting learning. Crediting Anderson and Collins (1988), they note that a considerable body of research on “formal features” like “pacing, audio cues, camera effects, animation, and editing techniques” has given us “a remarkably thorough understanding of how television promotes cognitive activities” (pp. 259–260). Moving beyond their discussion of these individual attributes, the authors highlight Gavriel Salomon’s (1972, 1974, 1979) seminal research on “filmic code” and his tantalizing suggestions about how motion media support learning in comprehensive ways. According to Salomon, filmic code—the “collective formal features of television as a symbol system unique to both film and television” (Salomon 1979, cited in Seels et al. 2004, p. 317)—represents information in a specific way that makes it necessary to process the information in a correspondingly specific way. The “formal features” of the code, taken as an integrated whole, create an information object that can be understood only through cognitive activity that takes those features and their relationships into account.

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In other words, fades, jump cuts, and other standard cinematic elements are not just “devices” but parts of an overall grammar that convey particular kinds of information in their own right—much as nouns convey information about persons, places, and things and verbs convey information about action. Learning from a film about the Battle of Gettysburg requires a kind of mental processing that reads and understands this grammar: the long shot of the battlefield as a whole, the close-up of General Lee’s stricken face, the somber music as night falls over the exhausted troops. To construct meaningful knowledge about the battle and its import, learners must be able to “read” the individual elements and account for the dynamism that connects them. Salomon (1979) theorized that children internalize the components of filmic code (e.g., zooming) and then use them to enhance their abilities to do this specific kind of processing. In motion media like film, cameras “zoom in” on key information, providing a model that learners can adapt to strengthen their own abilities to “zoom in” on important facts, concepts, and procedures. Moreover, according to Salomon (1979), learners who have integrated the “filmic code” into their overall cognitive processing skills can draw upon its components to perform specific cognitive activities when motion media trigger the need for such activities. The 2005 Oscar-winning film Crash, for example, tells its story in an associative, disjointed pattern rather than in a linear, straightforward one. Making meaning from the film both demands a specific kind of processing and activates the skills of the filmic code that are required to achieve it. Motion media, then, can enhance learning by “calling up” such skills as focusing, sequencing, and inferencing when these skills are required by a specific information object. Salomon is not the only theorist of “cineliteracy,” of course: Giannetti’s (2010) classic and comprehensive book Understanding Movies, for example, is now in its twelfth edition. What is important to this discussion, however, is the underlying assumption shared by scholars and film directors that the unique way in which information is represented in motion media is the most important characteristic of this class of media for learning. The “filmic code” itself determines the way that cognitive processing must proceed if moviegoers are to gain the most from watching a film—and, indeed, to become learners rather than only viewers. While Salomon’s ideas come from the world of instructional development and Giannetti’s come from the world of film studies, both clearly remind us that information—dynamic, complex, and multifaceted—provides the basic building blocks of learning. Both static and dynamic multisensory information objects support learners in both receptive and expressive tasks. Whether gaining information from a museum exhibit on dinosaurs or creating a “dinosaur habitat” diorama, whether learning about an environmental threat in South America from a commercial video or creating a video of pollution in their own neighborhoods, learners can focus on the most salient aspects of information to identify, extract, and construct into personal understandings. Although the learning potential of such “nontechnological” objects is not the focus of much research today, it is important to remember that they offer many learning affordances—such as immediacy, relevance, motivational power, and redundant sensory cues. Even simple, everyday objects can be part of engrossing


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information-rich environments that call on learners to draw upon the full spectrum of cognitive processes from remember to create and on the full range of types of knowledge from factual through metacognitive (Anderson and Krathwohl 2001) as they transfer information into personal representations of internalized knowledge (Marchionini 1995).

2.2.3 Interactive Information Objects Interactive information objects are those whose content is encountered through “some level of physical activity from the user, which in some ways alters the sequence of presentation” (Smaldino et al. 2008, p. 371). Although we often limit our use of the word “interactive” to describe computer-managed objects, the term also applies to information objects that depend on causative bodily actions other than typing and clicking—such as nonelectronic games, simulations, and interactive displays. All interactive information objects depend on some level of physical activity on the part of the user to determine or alter their progression and direction. Whether playing bridge at a neighbor’s kitchen table or searching for primary documents in the Library of Congress’s “American Memory” site, the user encounters information, makes a personal decision about its relevance and significance, and acts accordingly. The game is won or lost, the “best” information is found or missed, as a direct result of some physical action on the part of the user. Of course, the cognitive activity that guides the physical action is the key ingredient in learning with interactive information objects. The significance of the physical is that it enables the learner to control his or her encounter with information in a manner and to a degree beyond what noninteractive objects can afford. In fact, interactivity not only allows but requires the learner to be in control of the information encounter—cognitively as well as physically. Keeping track of trump is a necessary precursor to laying down the right card, for example, and understanding the characteristics of a particular historic period is a prerequisite for selecting the “right” political cartoon to make a key point in a multimedia report. Interactivity is thus the crucial cognitive characteristic of these information objects because using them requires active cognitive engagement rather than passive observation. Nothing happens in an interactive environment—physically or cognitively—without that engagement. Interactivity thus allows a level of user control that goes far beyond simple manipulation of information objects in place. It is the presence of this “locus of control” within the learner him- or herself rather than within the teacher or the information object that is at the heart of the most significant learning affordances of interactive information objects. For better or worse, user control is an inherent component of all such objects, whether “traditional” or electronic. Understanding its nature, respecting its power, and knowing how to capitalize on its possibilities and avoid its pitfalls are crucial skills for learning in an interactive information-rich environment. And the more sophisticated the environment, the more critical the skills.

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2.2.3.1 Nonelectronic interactive information objects Games are among the world’s oldest “instructional media,” having been used in ancient China to teach the skills of warfare. Although “a current problem . . . is the lack of well-designed [commercial] games for the classroom setting” (Gredler 2004, p. 573), learning games based on commercial offerings like Concentration and Jeopardy are common teacher-designed activities. In such games, students’ spoken answers determine how the game unfolds, whose turn ends or continues, and so on. Libraries, too, are increasingly incorporating games into their collections (Nicholson 2009)—not only to promote their social mission among patrons of all ages but to teach students “about inquiry, use of information resources, participation in knowledge-based collaboration, and other critical thinking skills” (Lipschultz 2009, p. 41). While a number of the games now appearing in schools and libraries are undoubtedly electronic rather than “manual,” it is important to remember that it is games’ inherent interactivity that provides key learning affordances, even in environments that are not technologically driven. Even the time-honored spelling bee offers the interaction and motivation that are intrinsic to games. Similarly, training programs often rely on nonelectronic simulations to help trainees learn everything from how to fight wars to how to rescue people injured by wars’ attacks. These activities allow participants to control their learning environments and to see what results emerge from their different decisions and actions. Training programs for the trades routinely involve students in experiences in which they interact with the tools and materials of their craft to weld components of pipes, install sections of ductwork, or build brick walls in simulation exercises. The learners’ physical actions determine whether various liquids and gasses move through the pipes and ducts in the right direction or whether the wall is straight or curved—or tumbledown. According to Gredler (2004), simulations often give learners “experience with complex, evolving problems . . . reveal student misconceptions and understandings about the content [and] provide information about students’ problemsolving strategies” (p. 573). Interactive displays have become so common in children’s and other museums that a facility without a collection of hands-on exhibits is considered an anomaly today. In Baltimore’s Port Discovery, for example, children—and curious adults— can work in “royal workshops” with a carpenter, a tailor, and others in an “Amazing Castle Community”; cook, serve, and figure bills for food in a “’50s-style diner”; and “climb, crawl, jump, slide, swing, and swoosh through our three-story urban tree house” (www.portdiscovery.org). Philadelphia’s National Constitution Center— “America’s most interactive history museum” (www.constitutioncenter.org)–allows visitors to wander among life-size bronze statues of the Constitution’s signers; try on robes “just like” those worn by Supreme Court justices; experience an awardwinning multimedia production; and see a beam from the World Trade Center, a Ku Klux Klan hood, a microphone like the one used by Franklin Delano Roosevelt for his fireside chats, and more. Environments like these are not only informationrich—they are as wealthy as Croesus. Such environments offer minute-by minute opportunities for learners to draw upon every kind of knowledge, from factual to metacognitive, and every level of


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processing, from remember to create, in the Anderson and Krathwohl (2001) taxonomy. They require visitors to use all their senses in exploring a vast array of information objects—print, drawings and posters, real objects, recorded sound, video clips, and computer programs—to develop comprehensive personal understandings of information that ranges from the simple to the complex. While there is little, if any, research on the range and depth of learning that occurs in such information-rich informal environments, it is clear that they hold great potential for engaging learners in actively constructing internalized representations of information that can be put to relevant use (Bransford et al. 2000, Marchionini 1995). 2.2.3.2 Digital information objects Today’s focus on interactive information objects centers on those that are enabled by computer technology in some way. The interactivity in these environments allows learners to enter personal responses to information, relies on algorithms that process those responses in a variety of ways, and results in feedback that relates directly to the initial response. Learners thus control the sequence of their movement through these information objects—sometimes choosing simple problems in a drill-and-practice math program, sometimes engaging in a highly specialized discussion on a blog or in a chat room, and sometimes navigating dozens of Web sites to find the answer to a specific query. No matter what the route, it is the interactivity that provides the key learning affordances of these digital information objects. At the time of his groundbreaking work in instructional media, Kozma (1991) could only begin to look at research findings related to digital learning objects: the use of the computer as a learning tool in schools was still very new, and existing research had focused primarily on programs that seem quaint by today’s standards. Even then, however, Kozma (1991) identified interactivity as the key “cognitively relevant characteristic” (p. 179) of what were then known as “computer-based learning” environments. Because of interactivity—the computer’s ability to manipulate content in response to individuals’ input—computers were able to support complex and individualized learning in ways that no instructional media had ever been able to do previously. Developments in technology, pedagogy, and design over the two decades since Kozma’s (1991) article have both validated and expanded his original insight. Today’s commercial packages for business and home use provide perhaps the best illustration of Kozma’s (1991) primary point. These packages routinely accept information in one form and present it almost instantaneously in another form: numbers entered into a spreadsheet, for example, become graphs and pie charts showing the relative proportions and percentages the numbers represent. Although this process appears quite simple, in fact it reflects a profound cognitive activity— transforming information from one kind of representation (numeric) to another (visual). Kozma (1991) argued that such computer-based transformations of information into different representations support students’ learning of abstract ideas. Seeing such transformations made before their eyes allows students to understand

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that the underlying information is identical, despite its particular representation: a $4,000 expense on a $40,000 budget statement is the same thing whether it is presented as a set of numbers or as a graph. By allowing students to apply a variety of rules and procedures to manipulate information in this way, the computer’s interactivity supports learners’ building of mental models that include abstract dimensions that transcend specific representations. Relatively few classroom applications of stand-alone computer technology have taken overt advantage of the learning affordance of transformation: programs that converted the data from temperature probes into bar graphs were once rather common, but few other possibilities for such transformations were explored before the emergence of the World Wide Web. Other learning affordances of interactivity, however, are well represented in virtually all stand-alone digital information objects—users’ control of pace, content, and level of difficulty; programs’ provision of immediate feedback and reinforcement; hardware and software’s patience and reliability in presenting the same content to a variety of users at different times. These learning affordances—all inherent in interactivity—put users in control of the way they experience and interact with the information that digital information objects contain. Stand-alone digital information objects are somewhat rare today: the capacities of the Internet/ Web environment have exploded the possibilities for learning with interactive materials, and many of the best of the stand-alone genre have migrated to that environment. Even the simple simulation game The Oregon Trail, for example—which began its life on a five-and-a-half-inch floppy disk with a black background, orange block letters, and stick-figure pioneers that looked like cave drawings—has evolved through several generations into a full-color, audio-supported, more-or-less realistic Web site (http://www.virtualapple.org/oregontraildisk.html). Games, as a genre, have been rediscovered as learning environments in the electronic world, as chronicled by the Digital Games Research Association, or DiGRA (http://www.digra.org). Gee (2003, 2005) has written widely on the instructional value of games, particularly electronic ones, while Squire et al. (2003) wrote some of the earliest guides for designing digital games for learning. Haystead and Marzano (2009) conducted a meta-analysis of over sixty studies on games that suggested that using classroom games was associated with a 20-point gain in student achievement scores, variously measured. Because many of the essential learning affordances of digital information objects remain the same whatever their setting, research on the precursors of Webbased materials still provides critical insights into the learning affordances of all digital information objects designed to foster learning. In fact, focusing specifically on stand-alone electronic information objects allows a deeper consideration of the basic affordances of interactivity without the distraction of considering the additional affordances of the Internet/ Web. Research on these more comprehensive venues often tends to assume the affordances of their interactive ancestors—rather than verifying them in this new environment—and scrutinizes a variety of additional factors instead. This oversight makes it particularly important for designers and educators to keep “traditional” affordances in mind when determining how well Internet / Web objects actually support learning.


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One particular category of stand-alone digital information objects is especially valuable in showing the link between information and learning. Variously known as microworlds (Rieber 1992, 2004); generative learning environments (Cognition and Technology Group 1991); open-ended learning environments (Hannafin et al.1994; Land and Hannafin 1996; Oliver and Hannafin 2001); and other “computer-mediated learning environments” (Hannafin 1992; Park and Hannafin 1993), these products share important pedagogical and technological characteristics. Most significantly, they all immerse learners in self-contained, integrated environments in which the learners direct their own interactions with information and with various kinds of supportive tools in order to understand ideas or to solve problems. Hannafin (1992) describes an example called MENDEL (Streibel et al. 1987): Students initially construct tentative hypotheses regarding individual genetics experiments, and the computer subsequently generates data consistent with expert notions of predicted outcomes. However, MENDEL neither instructs students in “correct” procedures nor solves the problem for them (even though an expert system is available to do so). Instead, the system provides expert advice to students on how to evaluate their own predictions and hypotheses and how to reassess their assumptions to test progressively more refined hypotheses (p. 58).

Although these “environments” have been largely superseded by their Webbased descendents, their learning affordances do not depend on widespread Internet connectivity. In fact, because they are self-contained, they avoid some of the issues that unfettered information access can introduce. The learning affordances of such systems depend on the richness of the information their designers include, the careful selection and organization of that information to focus learners on specific concepts and tasks, and the interconnectedness of the tools through which learners manipulate the information. Above all, these affordances depend upon their grounding in learner-centered activities that put learners in charge of their encounters with information and that require learners to engage actively with that information to direct and control the progress of their learning.

2.2.4 Additional Affordances of Digital Information Objects The list of learning affordances of digital information objects is long—the power of interactivity allows an enormous range of possibilities beyond the simple control of sequence, pace, etc., touted for the earliest computer-based educational programs. The list is also largely theoretical: the affordances are often so subtle and intertwined that doing studies to establish their value is a difficult task at best. Moreover, differences among types of objects as well as in the terms their experts use to describe them create challenges in determining “global” affordances while remaining true to the intricacies of each type. Even so, scholars have offered a host of actual and potential learning affordances inherent in digital information objects that do not depend on the wider linkages possible through the Internet and/or the Web. Reiber (2004), for example, argues

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that microworlds are intrinsically motivating, lead to immersive activity, and provide “doorways” to ideas by offering simple, structured examples that are immediately understandable to users. Because they include learning supports and readily accessible information, they reduce students’ cognitive load, enabling them to construct new understandings rather than focusing primarily on learning or remembering isolated facts and rules. Through guided discovery, students can therefore “experience and appropriate sophisticated ideas” (Reiber 1992, p. 93) that would otherwise be beyond their cognitive abilities. Land and Hannafin (1996) mention “opportunities to represent and manipulate complex, and often abstract, concepts in tangible, concrete ways”; determination by individual learners of “what, when, and how learning will occur based on unique goals and needs” (p. 37); and the opportunity for learners to “build and test their intuitive … notions about the world”—and to correct them when they prove to be in error (p. 38). Hannafin et al. (1994), in a summary article, gather a number of affordances discussed by others: among these are increased flexibility (Spiro and Jengh 1990; Spiro et al. 1991); improved individualization (Cognition and Technology Group at Vanderbilt 1991); scaffolding for discovery learning (Keegan 1995); manipulation of information, alteration of complexity, and creation of products (Perkins 1991); and support for higher-order cognitive skills (Roth and Roychoudhury 1993). Throughout the summary, Hannafin et al. (1994) add their own ideas as well: such environments provide access to multiple perspectives, allow learners to assume responsibility for their own learning, and enable learners to use personal experience as the basis for that learning. This type of interactive information object “immerse[s] learners in experience that assist[s] them in identifying, exploring, testing and modifying personal intuitions, beliefs, and models” (Hannafin et al. 1994, p. 50)—allowing both teachers and learners to focus on the process of learning rather than only on the outcomes of that process. Consider what some of these affordances might mean to someone learning stagecraft—either in a formal course or, perhaps, as a volunteer for a community theater. Immersed in a digital information environment, our learner might decide to focus on the Broadway musical. That choice directs the computer to an archive of scripts of famous Broadway musicals (rather than to an archive of, say, scripts of Shakespeare’s tragedies). Further choices would lead to other information—perhaps stage directions for a particular play, sketches of costumes worn in various productions, schematics showing the shapes and dimensions of performance spaces at various theaters, and descriptions of the sound and lighting configurations at those same theaters. Perhaps our learner—remembering the Broadway version of Cats seen several years earlier—had begun this exploration thinking that Cats would be a good offering for the local theater. But experience with the digital environment forces a revision of that theory: a tool within the system allows her to “block” the actors by creating visual depictions of their positions on stage in various scenes, and the blocking exercise convinces her that the play has too many characters to fit comfortably on a stage like the one in the local theater. She had already realized that the local theater’s lighting isn’t up to creating the special effects that enriched the


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Broadway production; actually blocking the production—a sophisticated activity made simple, accessible, and tangible by the system’s interactive tool—illuminates the issue from another perspective. Having been guided in this discovery about Cats by the tools and information in the system, our learner invokes higher-order skills in critical thinking and problem solving to construct a new, personal understanding of the situation. Perhaps she will explore another musical—or perhaps exploring those Shakespearean tragedies seems more attractive now. The system, of course, stands ready to support either option—and it reduces her cognitive load by “remembering” her path through the information and returning to the appropriate decision point. It is clear that the learning affordances in digital learning objects like this one are information-based: an information encounter is at the heart of each step in the learner’s interaction with these objects. Here, information consists of “objects in the world” like archives of primary source materials (Marchionini 1995, p. 5). Learning requires the use of all four types of knowledge (facts, concepts, procedures, and metacognition) at all six levels of learning: remembering facts about musical comedy, understanding theories of staging, applying rules and procedures to “block” the actors, analyzing discrepancies between preexisting knowledge and the knowledge revealed through guided discovery, evaluating alternative courses of action when the initial assumptions prove incorrect, and creating a new understanding of the intricacies of stagecraft (Anderson and Krathwohl 2001). Learners have a wealth of opportunities to work actively and creatively to construct “the components of internal knowledge in [their] minds” (Marchionini 1995, p. 5). Even simpler digital information objects like tutorials and small-scale simulations revolve around the key affordance of interactivity to engage learners directly with information in order to construct personal, individual understandings of the world. Although not all such objects tap all the potential that interactivity offers, the best of them enable learners to encounter a range of types of knowledge, from facts through metacognitive strategies, and to engage in many levels of learning as they monitor their self-directed progress and become increasingly informed. Because of interactivity and the learner control it affords, learners using such objects are ultimately in charge of constructing their own knowledge—whether that knowledge involves something as routine as mastering the times tables or as complex as creating a video for a social-media site. Content and process, internal and external, are linked through the wealth and complexity of ways in which digital information objects can nurture learners’ interactions with a dynamic, multifaceted information environment. Of course, interactive information objects support expressive tasks as well as receptive ones. In digital environments, we have become accustomed to thinking of word processors, spreadsheets, and drawing programs as built-in tools for communicating information we have generated. But even simpler interactive information objects offer unique affordances for communicating what has been learned. Stammering out the correct spelling of “antediluvian,” serving a new dish concocted in a cooking class, bandaging a simulated victim of a simulated terrorist attack—all these activities communicate knowledge directly and seamlessly within

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the information object itself. At some level, in both digital and nonelectronic interactive information objects, the communication is inseparable from the knowledge that underlies it: in terms of information theory, we might say that the drawing of a sunflower is a physical representation of a mental representation of the flower or that a bowl of chowder is a physical representation of the abstract mental representation of how to make it. Unlike any other information objects, interactive ones allow learners to become one with the environment in their communication of new understandings as well as their construction of them.

2.3

Conclusion

The world itself has always been an information-rich environment, but the range and array of information in the twenty-first century is more encompassing than ever before. Each day, we are surrounded by information objects that can help us learn about the physical, social, psychological, and spiritual aspects of our lives. Singlesense objects like magazine articles and radio talk shows invite us to learn through verbal and visual literacy. Some multisensory objects, like memorial walls dedicated to fallen heroes, invite us to learn by seeing, touching, and reading aloud their information—the names of fire fighters and police officers who died in the line of duty. Other multisensory objects, like television shows about endangered species, invite us to learn by using the sophisticated grammar of the filmic code as well as by seeing the pictures of animals and their habitats and listening to the narration that chronicles their decline. A simple interactive object like the board game Monopoly invites us to learn about economic realities in an informal setting, while countless digital information objects formally invite pre-schoolers to learn their ABCs, college students to learn pharmacology, and the rest of us to learn almost anything else that takes our interest. Seeing these everyday items as information objects promotes a particular way of thinking about learning: it encourages a direct focus on the kinds and levels of information inherent in each object that is encountered. It fosters a conscious attempt to identify and extract the parts of that information that are most relevant to the learning task at hand. When viewed as an information object, the state capitol is much more than an undifferentiated, old-fashioned mass of stone: it is a source of information about architectural styles, the craft of stone carving, and the symbols that were important to the society that erected the building. A forest is more than a setting for a pleasant afternoon stroll: it is an encyclopedia of information about different kinds of trees, birds, flowers, and other natural phenomena. Even something as mundane—and traditional—as a chapter in a textbook is not just an assignment to be endured but a source of specific facts, concepts, procedures, and strategies that a learner can remember, understand, apply, analyze, evaluate, and use to create new knowledge (Anderson and Krathwohl 2001). Information objects represent information in a variety of ways—statically, dynamically, interactively, or in combination. The different kinds of representation

2.3 Conclusion

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offer different learning affordances—the support for focusing on detail encouraged by static representations, the framework for developing understandings of processes inherent in dynamic ones, the scaffolding for moving seamlessly from receptive to expressive learning offered by interactive ones. These and the many other learning affordances cited throughout this chapter provide a rich buffet of ways to encounter and use information as the fundamental building block of learning. Learners in informal learning environments—the art gallery, the movie theater, the computer game—can certainly enhance their learning by exploiting the learning affordances available in those environments. In formal learning environments, where learning must be intentional rather than only incidental, taking advantage of every opportunity to enhance learning is far more important. In these environments, teachers and learners can increase the likelihood of learning by consciously choosing information objects that offer the best learning affordances for the task at hand. For example, the learning affordances of a three-dimensional model of the human heart make it a better choice for learning the difference between the right atrium and the left ventricle than a film of a beating heart. The affordances of the film, by contrast, make it more promising for learning about the healthy and unhealthy rhythms with which a heart can beat. The visual and tactile nature of the model will help learners remember the names of its components (facts). The film’s visual and verbal explanations, accompanied by its depiction of the motion of the beating heart, will help them understand the process (concept). By focusing specifically on the kind of information to be learned—fact, concept, procedure, or strategy—and the cognitive level of learning required—remember, understand, apply, analyze, evaluate, and create (Anderson and Krathwohl 2001)—teachers and learners can make informed choices about which information objects embody the learning affordances that are most likely to encourage particular kinds and levels of learning. While the choices are seldom so stark as the example suggests, the underlying idea of choosing information objects according to these aspects offers a promising approach to learning in an information-rich environment. In keeping with current learning theory, a focus on information as the essential building block for learning assumes that individuals are “goal-directed agents who actively seek information” in all their learning environments, formal and informal. Alone or with others, they “construct new knowledge and understandings based on what they already know and believe” (Bransford et al. 2000, p. 10). Seeing the world as composed of objects that carry the information we need for learning reinforces both the autonomy of the learner and the primacy of information. It suggests how information and learning converge—incorporating all the complexity and dynamism of each—in a merger of content and process, external and internal. Thinking of information as “objects in the world” that can “change a person’s knowledge” through a transferring of their meaning into “a person’s cognitive system, and . . . the components of internal knowledge in people’s minds” (Marchionini 1995, p. 5) deepens our understanding of both learning and information. Developing a habit of mind that focuses on the information inherent in the objects around us fosters the curiosity and cognitive engagement that are essential to learning in today’s global information-rich environment.

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References Anderson, D. R., & Collins, P. A. (1988). The impact on children’s education: Television’s influence on cognitive development. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement. (ERIC Document Reproduction Service No. ED 295 271) Anderson, L.W., & Krathwohl, D. R. (Eds.) (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s Taxonomy of Educational Objectives. New York: Addison Wesley Longman. Anglin, G. J., Vaez, H, & Cunningham, K. L. (2004). Visual representations and learning: The role of static and animated graphics. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 865–916). Mahwah, NJ: Lawrence Erlbaum. Barron, A. E. (2004). Audio instruction. In D. H. Jonassen, (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 949–978). Mahwah, NJ: Lawrence Erlbaum. Bishop, M. J., Amankwatia, T. B., & Cates, W. M. (2008). Sound’s use in instructional software to enhance learning: A theory-to-practice content analysis. Educational Technology Research and Development, 56(4), 467–486. Bransford, J.D., Brown, A. L., & Cocking, R. R. (Eds.), How people learn: Brain, mind experience, and school. Washington, DC: National Academy Press. Clark, R. C. (1983). Reconsidering research on learning from media. Review of Educational Research, 53, 445–460. Cognition and Technology Group at Vanderbilt. (1991). Technology and the design of generative learning environments. Educational Technology, 31(5), 34–40. Fletcher, J. D., & Tobias, S. (2005). The multimedia principle. In R.E. Mayer (Ed.), The Cambridge handbook of multimedia learning. (pp. 117–134). Cambridge, MA: Cambridge University Press. Gee, J. P. (2003). What would a state of the art instructional video game look like? Innovate, 1(6). Available at http://innovateonline.info/ Gee, J. P. (2005). What video games have to teach us about learning and literacy. New York: Palgrave Macmillan. Giannetti, L. (2010). Understanding movies. Boston: Allyn & Bacon. Gredler, M. E. (2004). Games and simulations and their relationships to learning. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 571–581). Mahwah, NJ: Lawrence Erlbaum. Hannafin, M. J. (1992). Emerging technologies, ISD, and learning environments: Critical perspectives. Educational Technology Research and Development, 40(1), 49–63. Hannafin, M. J., Hall, C., Land, S., & Hill, J. (1994). Learning in open-ended environments: Assumptions, methods, and implications. Educational Technology, 34(8), 48–55. Haystead, M. W., & Marzano, R. J. (2009). Meta-analytic synthesis of studies conducted at Marzano Research Laboratory on instructional strategies. Englewood, CO: Marzano Research Laboratory. Keegan, M. (1995). Scenario educational software: Design and development of discovery learning. Englewood Cliffs, NJ: Educational Technology Publications. Kozma, R. B. (1991). Learning with media. Review of Educational Research, 61, 179–211. Land, S., & Hannafin, M. J. (1996). A conceptual framework for the development of theories-inaction with open-ended learning environments. Educational Technology Research and Development, 44(3), 37–53. Lipschultz, D. (January/February 2009). Gaming @ your library. American Libraries, pp. 41–43. Marchionini, G. (1995). Information seeking in electronic environments. Cambridge, MA: Cambridge University Press. Mayer, R. E. (Ed.) (2005). The Cambridge handbook of multimedia learning. New York: Cambridge University Press. Nicholson, S. (January/February 2009) Library gaming census report. American Libraries, p. 44.

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Oliver, K., & Hannafin, M. J. (2001). Developing and refining mental models in open-ended learning environments: A case study. Educational Technology Research and Development, 49(4), 5–32. Paivio, A. (1986). Mental representations: A dual coding approach. Oxford, UK. Oxford University Press. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287. Park, I., & Hannafin, M. J. (1993). Empirically based guidelines for the design of interactive multimedia. Educational Technology Research and Development, 41(3), 63–85. Perkins, D. (1991). Technology meets constructivism: Do they make a marriage? Educational Technology, 31(5), 18–23. Rich, M. (2008). Literacy debate: Online, R U really reading? The New York Times. Available at http://www.nytimes.com/2008/07/27/books/27reading.html Rieber, L. P. (1992). Computer-based microworlds: A bridge between constructivism and direct instruction. Educational Technology Research and Development, 40(1), 93–106. Rieber, L. P. (2004). Microworlds. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 583–603). Mahwah, NJ: Lawrence Erlbaum. Roth, W. M., & Roychoudhury, A. (1993). The development of science process skills in authentic contexts. Journal of Research in Science Teaching, 30(2), 127–152. Salomon, G. (1972). Can we affect cognitive skills through visual media? A hypothesis and initial findings. AV Communication Review, 20(4), 401–422. Salomon, G. (1974). Internalization of filmic schematic operations in interaction with learners’ aptitudes. Journal of Educational Psychology, 66, 499–511. Salomon, G. (1979). Interaction of meaning, cognition, and learning. An exploration of how symbolic forms cultivate mental skills and affect knowledge acquisition. San Francisco: Jossey-Bass. Seels, B., Fullerton, K., Berry, L., Horn, L.J. (2004). Research on learning from television (Ch. 12). In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 249–334). Mahwah, NJ: Lawrence Erlbaum. Smaldino, S. E., Lowther, D., L., & Russell, J. D. (2008). Instructional technology and media for learning (9th ed.). Upper Saddle River, NJ: Pearson Prentice Hall. Spiro, R., & Jengh, J. (1990). Cognitive flexibility, random access instruction, and hypertext: Theory and technology for non-linear and multidimensional traversal of complex subject matter. In D. Nix & R. Spiro (Eds.). Cognition, education, and multimedia: Exploring ideas in high technology (pp. 163–205). Hillsdale, NJ: Lawrence Erlbaum. Spiro, R., Feltovich, P., Jacobson, M., & Coulson, R. (1991). Cognitive flexibility, constructivism, and hypertext: Random access instruction for advanced knowledge acquisition in ill-structured domains. Educational Technology, 31(5), 24–33. Squire, K., Jenkins, H., Holland, W., Miller, H., O’Driscoll, A., Tan, K. P., & Todd, K. (2003). Design principles of next-generation digital gaming for education. Educational technology, 43(5), 17–23. Streibel, M., Stewart, J., Koedinger, K., Collins, A., & Jungck, J. (1987). MENDEL: An intelligent computer tutoring system for genetics problem solving, conjecturing, and understanding. Machine-mediated Learning, 2(1 & 2), 129–159. Vygotsky, L. S. (1978). Mind in society: The development of the higher psychological processes. Cambridge, MA: Harvard University Press.

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Chapter 3

Information and Communication Technologies: The Penultimate Interactive Information-Rich Environment

Abstract Building on Chap. 2’s exploration of the natures and affordances of various information-rich environments, this chapter focuses specifically on the environment offered by the Internet and the World Wide Web. It extends the earlier discussion of interactivity to position it as the basic learning affordance of this penultimate information-rich environment, and it explains how interactivity underlies the capability of information and communication technologies to provide both logistical and conceptual advantages. Because research on the unique learning affordances of the Internet / Web environment is still relatively limited, the chapter draws on earlier research as well as on a strong theoretical base to suggest the inherent possibilities for learning with information available here. The chapter argues that many of the affordances presented in Chap. 2 apply in this environment and that several key affordances and combinations of affordances that are uniquely present here seem to hold special promise for twenty-first-century learning—distributed processing and collaboration, discourse strategies and distributed processing, and collaboration and discourse strategies. The ultimate information-rich environment, of course, is the world in which we live: people, places, things, experiences, conversations, and our internalized stores of knowledge all offer boundless types and levels of information we can use as the basis for learning. Talking with friends, family, mentors—and especially with those who disagree with us—can bring us to a range of new ideas. A walk through a neighborhood farmers’ market or a drive through the countryside of a faraway land presents sights, sounds, smells, and opportunities to touch various objects that can spark learning. Random thoughts that flit through our minds as we reflect on the day’s events can be the basis for new insights. Cultivating an awareness of the information inherent in the natural world within and around us is key to making the most of this ultimate, ever-present information-rich environment. Today’s penultimate information-rich environment is provided by the Internet and the World Wide Web, which together host an ever-expanding array of what


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3 Information and Communication Technologies: The Penultimate Interactive

have become known as ICTs—information and communication technologies. These tools enable the creation, recording, manipulation, and distribution of virtually all types of information, especially digital information. The Internet’s underlying structure, the Web’s graphic interface, and ICTs allow this environment to simulate the world itself—and in some cases even to improve upon it: one of its earliest and simplest ICTs, email, transcends the natural world’s limitations of time and space to connect us across continents and datelines. More recent ICTs, the “apps” available through mobile technologies, magnify and extend this connectivity exponentially. Through virtually unlimited access to information in all symbol systems and in all formats, instantaneous communication around the world among users of all kinds, and a wealth of special effects from full-color motion video to digital sound, the Internet / Web environment and its extraordinary amalgam of ICTs can bring us strikingly close to the physical, psychological, and social experiences we encounter in the everyday world. In terms of the focus of this book, we can say that this penultimate information-rich environment enables users to undertake the full range of interactions with information that can lead to learning. A particular genre highlights just how well the Internet / Web environment mimics the “real world” in which we live: even its popular name—the virtual world— suggests its similarity. Arguably the most popular of these “immersive virtual environments” is World of Warcraft, with its reported ten million users (www. WorldofWarcraft.com). This “massively multiplayer online role-playing game” takes full advantage of ICTs to create a “reality” so compelling that it includes its own economy and a “plague”; it even sparks worried conversations about the realworld problem of addiction (http://en.wikipedia.org/wiki/World_of_Warcraft). Second Life, the most popular “educational” virtual world of the early twenty-first century, may be far less robust and popular than its commercial counterpart, but it is no less compelling in the promise it offers for future learning applications. In both formal and informal learning settings, virtual reality’s exceptionally rich array of information formats and multiple ways to interact with information offer powerful opportunities for learning in the penultimate information-rich world of the Internet / Web (see McLellan 2004). Many other genres exist within the Internet / Web environment, but it is well beyond the scope of this chapter to discuss them. The focus here is on the ways in which ICTs as a whole provide a technological information environment that supports information-based learning as described in Chap. 1. Recent summaries of the research related to such topics as computer-mediated communications technologies (Pfaffman 2008); computer supported collaborative learning (Stahl et al. 2006); distance learning (Howard et al. 2005); hypermedia (Jacobson 2008); learning in online communities (Bruckman 2006); and synthetic learning environments (Cannon-Bowers and Bowers 2008) all provide important insights into learning within the Internet / Web environment. While their contributions to our understanding of learning with information lie outside the borders of this discussion, they offer many useful avenues for pursuing this broad topic.

3.1 The ICT Environment: Interactivity, Information, and Learning

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he ICT Environment: Interactivity, Information, T and Learning

Perhaps it is because ICTs’ “bells and whistles” are so compelling that interactivity—the key to this environment’s learning power—remains underdiscussed. In fact, however, the same kind of interactivity that undergirds all computerbased interactive learning objects is the basic “cognitive characteristic” (Kozma 1991) inherent in ICTs. Interactivity in the structure of the Internet and the interface of the Web supports the application of all Anderson and Krathwohl’s (2001) levels of cognitive processing—remember, understand, apply, analyze, evaluate, and create—to information from all the taxonomy’s types of knowledge—facts, concepts, procedures, and strategies. The ability of ICT hardware and software to accept, process, and respond directly to each individual’s input allows each learner to amass an array of information that is uniquely important to him or her. It is interactivity that supports learners—as individuals and when working with others—in encountering a variety of information objects and transferring various types of content representation into internalized knowledge (Marchionini 1995). Just as in other computer-based interactive information objects, interactivity in the ICT environment expands what the learner is able to do cognitively with the information he or she encounters. Jonassen et al. (1999) offered compelling discussions of this phenomenon when the Internet / Web environment was fairly new, and Dede (2009) has continued this discussion in some of his recent work. Quantitative differences in the amount and variety of information available through the Internet / Web, as well as qualitative differences in the sophistication of ICT tools, have an exponential effect on that expansion. Here, as the learner’s understanding grows, interactivity allows him or her to respond quickly and fluidly to that increased understanding by seeking new kinds of information in accordance with changing knowledge and needs. For example, a person newly diagnosed with cancer might begin to learn about his illness in this environment by trying to find the most basic, well-established information—probably text-based—about its specific form. He might then follow a link to learn about various traditional options for treatment—perhaps explained through video files. Then, he might follow another link to information about newer options under study—perhaps with computer models of how the treatment attacks particular cancer cells. As a result of what he’s learned, he might then move almost seamlessly to contacting the National Cancer Institute about enrolling in a clinical trial. In other words, it is the phenomenon of interactivity that undergirds his ability to marry the full range of Anderson and Krathwohl’s (2001) cognitive processes to the full range of their types of knowledge in order to generate personal, usable meaning from information. With ICTs, as with other digital interactive information objects but in a much more sophisticated way, interactivity thus helps learners traverse the border between content and process as they learn from each encounter with new information, refine their approaches to the next set of information they seek, and engage with that

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information at various levels as they construct and communicate individualized meanings. While even simple computer-based interactive information objects support this marriage of content and process, the Internet / Web’s vast array of information and the ease of navigating, manipulating, and communicating that information give this penultimate information-rich environment its unique power as a learning venue.

3.2

Learning Affordances of the ICT Environment

Because the ICT environment is so new, and especially because it is so multifaceted, only a few of its general learning affordances have been firmly established. Its inherent scope and magnitude and the variety of its opportunities for learning— from text-only online courses to social-networking sites with full video and audio capabilities to virtual reality options to the growing world of “apps”—make it difficult to tease out affordances that apply across the board. The rapid and ongoing improvement of various individual ICTs means that there has been no sustained opportunity to study and verify the learning affordances of this general environment in depth: one promising application appears, only to be outpaced within weeks or months by the next. Does anyone still use Telnet, the technology that revolutionized access to library and other resources not so long ago? While we await more detailed study of ICTs’ learning affordances to draw firm general conclusions, some of their affordances related to the use of information for learning seem obvious. At one level are those that are primarily logistical—rapid access to a range of information objects, for example; at another level are those that are considerably more complex and profound—the opportunity for widespread distribution of learner-created information products, for example. In many cases, the affordances of the ICT environment echo those provided by other environments, but with a range and complexity that exceed anything those environments can offer on their own. This chapter discusses what seems apparent today about the particular affordances ICTs bring to learning with information and speculates on the promise of the Internet / Web as the penultimate interactive information environment for learning both now and in the future.

3.2.1 Access to Information Objects Within the Internet and the World Wide Web As teachers and librarians well know, one of the most useful learning affordances of the ICT environment is access to self-contained information objects residing in the environment—databases, learning games, curriculum materials, etc. While it is important to value the extraordinary array of information objects available through

3.2 Learning Affordances of the ICT Environment

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ICTs, it is also essential to remember that each of these information objects has its own affordances that are separate from the affordances provided by ICTs. Each can be classified as either a single-sense, multisensory, or self-contained interactive object—and each carries the affordances of its specific genre. A database of the works of Emily Dickinson, for example, is in essence a single-sense information object that can support the same kind of serial processing that any print object is able to support. Similarly, a streaming-video production on the life cycle of the butterfly is essentially a multisensory information object that can support learning about the process of metamorphosis; finally, a self-contained simulation about flying a space shuttle is essentially a stand-alone interactive information object that supports learning the abstract concepts represented by the cockpit’s gauges and dials. Each of these objects has the specific learning affordances of its type, and the examples above show that the wider interactivity of ICTs often serves primarily as a mechanism for access and delivery to the full range of these types of information objects rather than as a special learning affordance of its own. Each of the thousands of information objects found in MERLOT (Multimedia Educational Resource for Learning and Online Teaching), for example, retains its own affordances as a reference document, a tutorial, a simulation, and so on—independently of the MERLOT service itself (http://www.merlot.org). While search engines and directories offer unparalleled ways to locate such self-contained information objects, it is important to identify the essential nature of the information in each kind in order to exploit its earning affordances in the most efficient and productive ways. The affordances discussed in Chap. 2, then, are as relevant to the individual information objects in the ICT environment as they are to stand-alone information objects.

3.2.2 Learning Affordances Unique to ICTs Beyond the “logistical affordance” of providing access to individual information objects, it also seems clear that the Internet and the Web also offer special learning affordances of their own. Although research has substantiated only a few of these (Hill et al., 2004), excitement flourishes both within and beyond the informationstudies and instructional-design communities about the possibilities for learning inherent in this richest of the technological information-rich environments. Educators are especially intrigued by the opportunities it affords for meaningful, higher-level learning. Discussions of critical thinking, problem solving, case-based reasoning, collaborative learning, and truly independent learning abound in both the popular and the scholarly literature about ICTs. (See, for example, Belland et al. 2008; Dede 2009; Hung et al. 2008; and Jacobson and Azevedo 2008.) The possibilities for such learning stem from the convergence of rich content and sophisticated tools for manipulating that content with unparalleled interactivity, as noted above. This convergence reflects another blending as well—the interweaving

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of information and learning explored in Chap. 1. Within the ICT environment, it is clear that both information and learning are dynamic, complex, and multifaceted and that information is the essential building block from which learners construct meaning as they traverse various resources. Consider, for example, the patient investigating cancer treatments noted above, the traveler planning a trip to the latest trendy destination, or the student completing a research assignment on black holes—a topic for which new information becomes available almost every day. Immersed in the fluid environment of the Internet / Web, these learners are engaged in an experience in which it is difficult to pinpoint where information ends and learning begins. Enhanced possibilities for higher-level learning also stem from the fact that the ICT environment can “carry” some of the content and tasks that hobble many learners, freeing them to focus on more advanced concepts and processes. While all technologies free us from carrying content to some degree—the pencil allows us to take notes rather than remembering minute details, for example—none does it as thoroughly and as efficiently as the Internet / Web. Here, at least two of Anderson and Krathwohl’s (2001) types of knowledge—factual and conceptual—are or can be embedded in the technology so that learners can reference them readily rather than trying to remember them. This embedding frees the learner from having to focus on basic forms of information and allows him or her to focus more directly on the more advanced types—procedural and metacognitive. The simple spreadsheet offers the most obvious example: its ability to compute and re-compute values instantaneously allows budget officers, corporate planners, and university researchers to focus on analytic, evaluative, and generative tasks while the spreadsheet takes over the more tedious computational ones. In sophisticated applications—like PASW (formerly SPSS) for statistical analysis and concordance programs like WordCruncher that support narrative analysis—procedural knowledge can also be embedded, allowing the learner to move directly to apply the metacognitive strategies required to think critically or to solve the problem. Determining which specific PASW application to use for a particular data set and research question requires analysis and evaluation, for example, while coming to terms with John Steinbeck’s novella The Red Pony requires creating new meaning from the data the computational analysis reveals about motifs and other patterns. Similarly, the embedding of factual and conceptual knowledge (Anderson and Krathwohl 2001) can also free the learner from focusing only on the lowest levels of learning—remember and understand—in order to engage more fully and effectively with the advanced levels—apply, analyze, evaluate, and create. With a few clicks, our cancer patient can locate the contact information of various specialists and focus on analyzing who is best for him rather than spending time finding a slate of potential doctors; our traveler can refer to Web-based maps, pictures, and reviews and spend his time evaluating which hotels to book rather than gathering an array of travel guides and magazines; and our student can readily review the latest data from federal or other studies and focus on creating her own model of a black hole rather than spending hours tracking down the most recent information. Taken together, the Internet / Web’s combination of content, tools, and interactivity

3.3 Theory and Research on ICTs’ Learning Affordances

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provides a set of “cognitive characteristics” (Kozma 1991) that offer the possibility of achieving kinds and levels of learning with ICTs that have not been achieved in any of their ancestral environments.

3.3

Theory and Research on ICTs’ Learning Affordances

Research on the relationship of the Internet / Web to learning has filled the instructional-design literature in recent years: a Google search on “learning and Internet” yielded almost forty-five million hits. One single publication in particular— the Encyclopedia of Distance Learning (Howard et al. 2005)—further illustrates the scope of the research in this arena: it consists of four volumes, 317 articles, and over 2000 pages. Overall, however, “there has been surprisingly little empirical research documenting student knowledge gains associated with the use of educational [ICTs], and there have been few attempts to generalize theory or researchbased design principles for these types of systems” (Jacobson 2008, p. 6). As noted above, the environment is still too new, fluid, and variable and its questions are too numerous and complex to yield the kinds of deep and enduring conclusions and theories that, say, Salomon (1979) developed in regard to learning with film. Some theories and principles, however, are beginning to emerge. In 2004, Hill et al. identified a number of promising research fronts that might one day lead to “develop[ing] a clear understanding of the impact these technologies have had and are having on the processes of learning” (p. 433). Drawing on their comprehensive review of the literature on “Internet-based learning,” the authors identified four “activities” and two kinds of “strategies” that are especially noteworthy in the context of learning with information. One of the activities—knowledge construction— is, in fact, the information-based learning to which all the others contribute: “The learner is . . . actively involved in constructing something unique based on what is uncovered as they [sic] use the Internet” (Hill et al. 2004, p. 445). Two of the other three activities—information gathering and using distributed resources—describe learning affordances that are obviously information-based. The first speaks to assembling information; the second, to using information products available from different places. The third activity—distributed processing— and the two kinds of strategies—collaboration strategies and discourse strategies—represent ways in which learners engage with information objects, often with other learners, to accomplish these information-based tasks. These five factors are either unique to the ICT environment or uniquely enabled by it. Taken together, they seem to comprise a set of cognitive characteristics (Kozma 1991) that are inherent in and exclusive to ICTs. That is, they suggest an emerging taxonomy of learning affordances related specifically to this penultimate information-rich environment. The ways in which the factors contribute to the ultimate “activity” of knowledge construction with ICTs are multifaceted. Two seem to have a fairly well-understood influence on learning: information gathering and using distributed resources can

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occur more readily with ICTs, but the cognitive processes involved in each reflect similar processes in other environments. These two factors relate primarily to access to self-contained information objects residing in the environment, as noted above, and they are generally associated with individuals’ personal learning— generally, receptive learning. The final three factors, however—distributed processing, collaboration strategies, and discourse strategies—are qualitatively different in an ICT environment, largely because they support knowledge construction within groups in ways that were impossible before such technological tools appeared and because they go beyond receptive learning to demand expressive learning as well. These three factors, then, seem to offer the most promising arena in which to examine what “new” entries this environment adds to a catalog of learning affordances. Individually, each of the three offers its own support for learning with information: distributed processing allows learners at dispersed locations to engage with segments of information, collaboration strategies allow learners to put those information segments to use in the service of a whole that’s greater than the sum of its parts, and discourse strategies allow learners to communicate information about both their processes and their products. In combination, the three suggest not only unique ways that the ICT environment can support both receptive and expressive learning—but ways in which this penultimate information-rich environment can transform learning processes and outcomes by integrating individual sense-making and group knowledge construction in as-yet-unknown ways. In practice, of course, the three factors intertwine in inseparable ways; discussing their various combinations separately, however, helps to distinguish what each brings to the overall learning environment.

3.3.1 Distributed Processing and Collaboration Both distributed processing and collaboration have long histories within well-established educational venues, and their combination provides the foundational learning affordance of the penultimate information-rich environment of the Internet / Web. Distributed processing involves individual efforts by learners in various locations who are responsible for different cognitive tasks inherent in a particular learning project. To complete the project, the learners collaborate in various ways to refine and integrate the results of their individual work. The entire process is uniquely enabled by ICTs’ widespread connectivity, vast stores of information, and sophisticated communication tools. The global interconnectivity enabled by this environment allows the “social construction of knowledge” (Lave and Wenger 1991) to engage far greater numbers of individuals than is possible with any other information-rich environment, even the natural world. People with vastly different perspectives, backgrounds, and goals can use ICT tools and resources to work together to construct bodies of knowledge that are not only meaningful to individual learners but “owned” by the group.


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Perhaps the most evident examples of ICT-enabled distributed processing and collaboration for learning occur through online courses. Using course management systems like Blackboard and Sakai, faculty design presentations and assignments that take advantage of ICTs to engage students in a range of activities: posing discussion questions that encourage (or require!) collaborative discussion among students at widely dispersed locations through chat, blogs, and wikis; hosting guest experts who can involve those same students in discussing cases and hot-button issues in order to construct understanding as a group; structuring projects in which those students collaborate to produce single-sense, multisensory, or interactive presentations of their own. Although such systems are still generally “klunky” and suffer from a lack of pedagogical guidance and from both teachers’ and students’ status as relative novices in exploiting their possibilities, those possibilities seem endless—at least in theory. Whether synchronous or asynchronous, online learning tools inherently encourage and expressive learning; promote time-on-task behavior beyond class hours; and allow distributed processing and collaboration to occur in response to participants’ needs rather than in response to room availability, parking fees, and other constraints of brick-and-mortar environments. Ways in which the ICT-based affordances of distributed processing and collaboration can support the construction of knowledge from information are rapidly emerging. Some possibilities, like collaborative learning, are rooted in long-held understandings of the learning process; others, like the interactions enabled by the World Grid, have few direct precedents and take us beyond what the human mind can do alone. Across this very broad spectrum, applications embody the full range of types of knowledge and levels of learning described in Anderson and Krathwohl’s (2001) Taxonomy. Collaborative learning—which generally involves a kind of “distributed processing” without benefit of computers—has a long history in American education (e.g., Cohen 1984; Slavin 1995). The kind of collaboration enabled by ICT tools and resources, however, goes well beyond the collaboration that typically occurs among groups working together in single, isolated settings. Even at a basic level, the ICT environment can enhance learning by distributing simple learning tasks and communication strategies across time and space: elementary students in one classroom frequently communicate with electronic penpals in other countries to teach one another about their lives in order to develop common understandings of the world they share. ICTs can enhance more ambitious attempts at collaborative learning as well. Consider, for example, the well-established “jigsaw” model (Aronson et al. 1978), in which each student in a six-member team does research to become an “expert” on one part of an assignment, meets in a group with representatives from other teams who have developed “expertise” in the same area, and ultimately shares this group expertise with the members of his or her original team. At the level of the individual classroom, the jigsaw has been shown to be quite effective, both cognitively and socially (Hanze and Berger 2007; Perkins and Saris 2001; Walker and Crogan 1998). Imagine, for example, the rich learning experience of American elementary students who master and communicate their knowledge of the geography,

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history, customs, exports, art, and music of Japan—and the even more sophisticated experience of their older brothers and sisters in high school who work on each of those areas at a deeper level. As sophisticated and effective as this approach can be, however, it is generally bounded by the physical limitations of class time and classroom space. ICTs can enhance students’ opportunities for learning by drawing in more extensive information on which to base expertise, by expanding the groups themselves, by hosting groups that span continents rather than classrooms, and by using electronic means to communicate each learner’s emerging understandings. Processing can be distributed to take advantage of students’ varied cultural backgrounds, enriching their collaboration and communication and, ultimately, their learning. Imagine, once again, a jigsaw project involving Japan—but this time with both American and Japanese students learning about each other in an ICT-enabled jigsaw project. This time, the resources could include materials uniquely available in each country, the participants could include students who have lived experience of the cultures of both societies, the groups could include combinations of Japanese and American students working together, and the participants could use either a blog or a wiki to exchange ideas about questions and topics of interest. At a more sophisticated point on the spectrum, the Jason Project illustrates how the Internet and the Web can enhance learning not only by involving students but also by allowing “students, teachers, and researchers [to] work side by side to learn about the delicate systems of marshes and swamps, the creatures that call the wetlands home, and the lively, hard-working people who make their living on the bayous of Louisiana” (www.jasonproject.org). Experiences like those provided by the Jason Project—which brings together information problems, a range of resources, and experts who serve as mentors—enable learners to mimic the “real-life” collaboration of journalists, researchers, and scholars around the world. The day-today work of such professionals in the twenty-first century involves sharing information through ICTs to understand political developments in the Middle East, to analyze weather patterns and their meanings, to create and evaluate new treatments for diabetes, and more. One of the earliest but most striking examples of the learning value of this enhanced collaboration occurred as part of Dan Buettner’s 1995 “interactive expedition” entitled MayaQuest, in which students around the United States watched as a team of “explorers” biked through Central America in search of insights about Mayan civilization (see Buettner and Mason 1996). The team uploaded information from laptops to satellites twice a week, and students in classrooms that had subscribed to the quest engaged with the team and the information they supplied. At one point, the team met with archeologists in the Amazon who had unearthed a glyph and who engaged in an actual—not staged—argument about its meaning. Students were invited to suggest their own interpretations while the stone was sent to linguists in Texas to solve the problem. When MayaQuest revealed the meaning of the glyph, participants learned that neither of the experts at the site had been correct—and that a student had suggested the correct meaning. This collaborative examination of information from several perspectives enabled the students to


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collaborate directly in the discovery of knowledge—not merely its presentation— and to learn how science actually works and knowledge advances. Bicycles and spotty satellite connections have given way to far more advanced technologies in recent years, and early “learning communities” like those engaged by Buettner and his colleagues have exploded into online communities that engage scholars and students around the world in sophisticated information gathering and analysis to advance their own and others’ learning. Examples, both large and small, are plentiful; research and scholarship about such communities abound (see, for example, Barab and Duffy 2000; Barab et al. 2004; Riel and Polin 2004). Distributed processing is a defining characteristic of all online learning communities, and the specific ways in which different kinds and models support individuals’ learning and the construction of shared knowledge are a matter of much speculation and research (see, for example, Bos and Shami 2006; Tutty and Klein 2008). It is clear, however, that these communities support the full range of Anderson and Krathwohl’s (2001) kinds of knowledge and levels of learning—particularly the analysis and evaluation of information and the creation of new knowledge. Of all such projects, it is perhaps the World Community Grid that offers the most intriguing example of distributed processing and collaboration. Based on the concept of grid computing—that is, the use of multiple, dispersed computers to attack a single problem—this project relies on both dispersed computers and dispersed humans to advance knowledge. In keeping with its mission “to create the largest public computing grid benefitting humanity,” the Grid’s network of scientists (including students), community advisers, and administrative and technical staff work in teams distributed around the world to tackle such issues as growing more nutritious rice and finding a cure for HIV/AIDS. The humans’ distributed processing and collaboration guides and directs the project’s advances, and computers around the world support their efforts: special software on volunteers’ computers allows the volunteers to “request data on a specific project from the World Community Grid’s server. It will then perform computations on this data, send the results back to the server, and ask the server for a new piece of work” (http://www. worldcommunitygrid.org/). The new data goes into the knowledge base on which the project’s humans draw to reach new understandings and move ahead. The project thus provides a stunning example of distributed processing that transcends the boundaries between humans and machines and beautifully illustrates Kozma’s (1991) insight that we learn with media rather than from them.

3.3.2 Discourse Strategies and Distributed Processing Discourse that can lead to learning is embedded in the very nature of ICTs—whose middle name, after all, is “communication.” While Hill et al. (2004) limit their treatment of discourse in Internet-based learning to various kinds of online discussion, it is important to remember that “traditional” information and communication technologies as diverse as the single-sense telephone and radio, the multisensory televised

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lecture, and the self-contained interactive CD-ROM database are all dependent upon discourse. Certainly, when such tools join the march to the Internet—for example, the telephone application Skype (http://www.skype.com), podcasts and vodcasts from National Public Radio, and Web-based databases—discourse remains at the heart of their learning affordances (see Chen and Wang 2009; Hew 2009). Whether they offer only one-way dissemination of information or true discourse, these tools depend upon the clear and compelling communication of information for their effectiveness. Discourse strategies and distributed processing are at the heart of the learning with ICTs described above: from jigsaws to Jason, it is through discourse that the ideas generated through distributed processing and collaboration are exchanged and explained. The expansion of time, place, and people enabled by today’s ICTs requires extensive and sophisticated discourse for learners both to gather and build a range of ideas and then to refine and consolidate them into some kind of shared meaning. The widespread and sophisticated distributed processing enabled by ICTs thus underlies the unique learning affordances related to discourse in this environment. The opportunities for distributed processing and discourse seem almost unlimited with ICTs, and the variety of possibilities makes it difficult to suggest only a few examples. From the 140 characters in “tweets” to the gigabyte files in an interactive training package like Wimba (http://www.wimba.edu), these affordances underlie learners’ abilities to develop ideas independently and to share and amalgamate them—or refute them—within local or even global networks. The research literatures on communication in general and on computer-mediated communication and computer-supported collaborative learning in particular offer a powerful conceptual framework for studying how distributed processing and discourse work together to aid learning with information in the ICT environment. While it is beyond the scope of this chapter to provide a comprehensive review of this vast literature, research in this area clearly holds promise for expanding our understanding of how individuals and groups use these affordances in knowledge construction. In Anderson and Krathwohl’s (2001) terms, such research might ask what the patterns of discourse in online learning environments tell us about how learners remember the math facts they encounter in an environment like the Math Forum (www.mathforum.drexel.edu), understand the concepts that grow out of these facts, apply relevant procedures to solve math problems, and use metacognitive knowledge to govern the process. Even more interestingly, such research might ask how learners use distributed processing and discourse to analyze mathematical information, evaluate it, and use it to solve problems and create new understandings. (For more information on the Math Forum, see Stahl 2009.) As formal online learning environments become increasingly sophisticated in the options for distributed processing and discourse they support—including blogs and wikis with increasingly robust features, for example—there will continue to be much to learn about patterns of distributed processing and discourse in these evolving tools. Outside the formal educational environment, ICTs offer additional opportunities for the combination of distributed processing and discourse that lend themselves to knowledge construction. Applications like Google.docs (www.googledocs.org) and


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SharePoint (http://sharepoint.microsoft.com), for example, allow writers to develop and exchange drafts as they work through group projects, sharing one another’s drafts of different segments of the project and collaborating online to refine and enrich the individual components while ensuring that the final product is well integrated and coherent. Such tools can readily be incorporated into formal environments as well in order to provide learners with “real world” opportunities to master emerging ICTs while building knowledge with information. Consider once again our example of collaborative learning about Japan: one learner (perhaps the class expert on climate) might offer ideas, others might pose clarifying questions that require refinements in the information and/or its presentation, still others might offer extensions to the developing paragraphs from their own areas of expertise, and so on. Over the course of the project, group members might not only work on one another’s drafts but gain the information they need to engage in peer review and analysis of their work (see Trautmann 2009). Activities like these are uniquely enabled by today’s ICTs, and tomorrow’s versions will undoubtedly offer even greater facilitation for distributed processing and discourse. Opportunities to engage these affordances in both formal and informal learning environments are appearing rapidly but are just beginning to be understood. The search for discourse patterns and activities that are unique to using information as a tool for expressive learning in the ICT environment is only at its dawn.

3.3.3 Collaboration and Discourse Strategies Collaboration and discourse strategies undergird the most exciting possibility for learning with information in the ICT environment: the actual creation of knowledge, the highest learning activity in Anderson and Krathwohl’s (2001) Taxonomy. From the collaborative creation of a sixth-grade group multimedia report on the country of Ghana to the “rip, mash, and burn” products that result from the collaboration and discourse of highly sophisticated multimedia producers, these affordances uniquely contribute to the creation and dissemination of shared knowledge—an informationbased phenomenon that we are just beginning to appreciate and understand. The online encyclopedia Wikipedia is probably the most famous example of this construction of shared knowledge. Begun in 2001, by 2008 Wikipedia had become “the largest reference website on the Internet. The content . . . is free, written collaboratively by people from all around the world. . . . anyone with access to an Internet-connected computer can edit, correct, or improve information throughout the encyclopedia . . . (with a few minor exceptions, such as protected articles and the main page)” (www.wikipedia.org). Without question, Wikipedia democratizes the creation and communication of information. Despite its drawbacks—errors in its information have been both inadvertently and deliberately introduced by some members of the Wikipedia community—it remains a prime example of how collaboration and discourse can lead to learning at the highest level in a widespread, exciting, and popular way.

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An example that comes closer to the formal learning enabled by ICTs in general and collaboration and discourse strategies in particular can be found in the Victorian Web (www.victorianweb.org). Begun by George Landow at Brown University in 1986 with technologies that were precursors to today’s ICTs, this innovative “course project” set the stage for creating the kinds of learning possibilities available now. Through what was known as the “Intermedia Dickens Web,” students in one semester’s class posted the results of their assigned literary investigations, students of the following semester’s class posted their own work and commentary on their predecessors’ contributions, and students of many other classes and semesters continued the process—creating a collection of individually developed yet shared scholarly knowledge that ultimately led to the Victorian Web. Today, over twenty years after Brown’s ground-breaking work, the site includes resources in almost two dozen categories—from the visual arts to philosophy, gender issues, and science. Using technology that was primitive by today’s standards—but instructional creativity that was progressive by any measure—Landow and his colleagues led the way to an extraordinary collection of scholarship that is dynamic, organic, and original. It would be impossible in its current state without the learning affordances of collaboration and discourse found in today’s ICTs. Examples of today’s ICT-enabled information-rich environments are numerous and ever-changing, and any list of particular examples inevitably overlooks other exciting innovations. Two projects that have particular relevance to the affordances of collaboration and discourse strategies, however, are the Trans-Atlantic Slave Trade Database (www.slavevoyages.org) and SouthComb: A Southern Studies Community (www.southcomb.org). Both are growing and evolving under the leadership of Emory University, and both point the way for researchers, theorists, and learners to reach a deeper understanding of learning with information in the unique ways enabled by ICTs. Nor surprisingly, both provide opportunities for learners to use all the kinds of information content in Anderson and Krathwohl’s (2001) Taxonomy (facts, concepts, procedures, and metacognitive knowledge) and to do so at all the Taxonomy’s levels of learning (remember, understand, apply, analyze, evaluate, and create). The Trans-Atlantic Slave Trade Database is the culmination of work begun in the 1960s, when several scholars began to collect and encode data about the slavetrading voyages of the sixteenth through the nineteenth centuries. Over the years, scholars at a variety of universities, from several continents, and with support from a series of funding agencies created a database on almost 35,000 slaving voyages— including not only schedules and routes but also the names, genders, origins, and places of embarkation for over 67,000 Africans aboard the ships. The site provides access to tables and charts created by experts as well as to tools that allow individual learners to perform a variety of operations on their own. Users are invited to “create listings, tables, charts, and maps using information from the database” and to “use the interactive estimates page to analyze the full volume and multiple routes of the slave trade.” While “create” and “analyze” are the only terms from the Taxonomy (Anderson and Krathwohl 2001) mentioned specifically on the home-page, the context makes it clear that all the Taxonomy’s types of knowledge and levels of learning are embedded in these tasks.

3.4 Conclusion

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SouthComb, a newer venture, is an ICT-enabled community devoted to scholarship about the fourteen Southern states, from Virginia to Texas and from West Virginia to Florida. It houses almost one million records (including websites, directory entries, archives, and news sources) and addresses over a dozen topics (not only the usual suspects like “literature and language” and “music” but also such potentially surprising ones as “foodways and traditions” and “sports and leisure”). It is linked both conceptually and electronically to the journal Southern Spaces, whose editorial board is composed of scholars in the field who provide guidance for the project’s work. One of SouthComb’s most intriguing innovations is a faceted search system (Halbert 2009), which allows users to access records of interest through searching on a variety of facets, such as “state” or “subject.” Here, then, information science truly meets instructional design: “faceted searching,” a standard phrase in the information-science lexicon, becomes a key strategy for accessing materials that students can use to support their learning. The skillful and imaginative ways in which these two projects intertwine collaboration and discourse strategies offer exciting examples of the possibilities that ICTs provide as tools for information-based learning at all levels of Anderson and Krathwohl’s (2001) Taxonomy, particularly the highest one. Not only do these “digital collections plus” environments illustrate how scholars and experts can create knowledge, they offer others the chance to participate in the creation of knowledge themselves: generating even a small chart or map based on information in a database or adding even a single record to a collection of scholarship is an exciting and powerful example of creating knowledge. By joining the knowledge-creation communities developed by such projects, both advanced and relatively naïve learners can take advantage of unprecedented opportunities to be active creators of knowledge from information. These opportunities are undergirded by the unique affordances of sophisticated ICT-enabled information-rich environments like the two projects described above. While they are particularly strong examples of the “collaboration and discourse strategies” affordances, these projects obviously exemplify all the others as well. They embody both the two straightforward affordances of information gathering and using distributed resources (which support receptive learning) and the three more complex ones of distributed processing, collaboration strategies, and discourse strategies (which support expressive learning). All these affordances—on their own and in their various combinations—are unique to the ICT environment or uniquely enabled by it. As a result, this environment offers unprecedented opportunities for learning.

3.4

Conclusion

From the simplicity of email to the complexities of virtual reality, emerging mobile applications, and tool-enhanced digital collections, the Internet and the World Wide Web constitute today’s penultimate information-rich environment. Here, anyone can encounter almost unlimited information and point and click to engage with it

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in ways that mimic such “real” activities as shopping for a bargain, visiting an island, and even setting up a date for Saturday night. Here, anyone can find an extraordinary range of learning opportunities as he or she encounters information, manipulates it, and shares it with others. Researchers, theorists, teachers, and learners alike are at the dawn of exploiting this exciting and evolving environment as a learning venue. Although we are still discovering the kinds of thinking and knowledge construction the Internet and World Wide Web support, some underlying ideas about its learning affordances seem clear. The first, of course, is interactivity, which underlies all the others. Even now—decades after Kozma’s (1991) insight about the power of interactivity for learning—this affordance is little understood. We still have a great deal to learn about how to harness all its aspects in ways that will enhance learning in even “simple” computer-based interactive information objects, let alone how to leverage it fully through the additional affordances provided through ICTs. As the foundational “cognitively relevant” characteristic of today’s most compelling information-rich environment, however, interactivity is clearly one of its significant affordances. Building on interactivity, some other affordances specific to ICTs also seem clear. Logistical affordances—ways in which ICTs provide quick, easy, and flexible access to a range of information objects—are an easy starting point. These self-contained objects are independent of the ICTs themselves, but learners use ICTs to locate them. When enriched by visuals, the objects incorporate the affordances of multisensory information objects as described in Chap. 2. When they support interactivity within themselves, they incorporate the affordances of stand-alone interactive information objects, as also described in that chapter. The objects themselves might be very compelling, but they stand outside the conceptual affordances that are unique to ICTs. And it is those conceptual affordances that are the most intriguing. At this point, they are speculative, not grounded in the decades of research and theory that underlie the affordances cited for their technological ancestors. Hill et al. (2004), however, suggest six overlapping factors that provide a useful starting point for identifying affordances that are distinctive within the ICT environment. The two simplest—gathering information and using distributed resources—are enhanced by ICTs but are not unique to them. Essentially, ICTs support faster and more efficient ways for individuals to assemble information and use shared resources, but they do not change the basic natures of these activities. Hill et al.’s (2004) three more complex factors, however—distributed processing, collaboration, and discourse strategies—are unique in the ICT environment or are supported by that environment in ways that are potentially transformative. They suggest affordances for expressive learning that are characteristic of this environment and that enable knowledge construction—Hill et al.’s sixth factor—in unprecedented ways. They open the door to for individuals to apply, analyze, evaluate, and synthesize information in dynamic interaction with others in order to create shared knowledge that goes beyond what the individuals could achieve alone. Both separately and in combination, these factors tap into all the kinds of knowledge and levels of learning outlined in Anderson and Krathwohl’s (2001) Taxonomy.

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As ICTs improve and expand, more learning affordances are sure to emerge: while there is considerable excitement about the possibilities inherent in social media, online gaming, virtual reality, and the burgeoning number of downloadable “apps,” the learning affordances particular to these venues remain largely unknown. The race toward smaller, faster, more powerful, and more mobile devices will also spur the identification of new learning affordances: what is unique about learning to play a guitar on a cell phone? How will linking GPS devices to cultural info—telling an art student, for example what’s at the Philadelphia Museum of Art today—change and challenge learning? Each year, the New Media Consortium and EDUCAUSE release a Horizon Report that highlights technologies to watch in the coming years—and each year the report prompts educators to consider what a list of astonishing new developments will offer and require. What, for example, will “augmented reality—the concept of blending (augmenting) virtual data . . . with what we see in the real world for the purpose of enhancing the information we can perceive with our senses” (New Media Consortium and EDUCAUSE Learning Institute 2010, p. 21) mean for learning? How will “visual data analysis,”which “blends highly advanced computational methods with sophisticated graphics engines to tap the extraordinary ability of humans to see patterns and structures in even the most complicated visual presentations” (ibid., p. 29) affect what learners do? Perhaps most importantly, the increasing use of ICTs to support learning in groups will raise questions about the nature of learning itself. Ultimately, of course, learning is an individual phenomenon: each learner constructs a uniquely personal understanding of the world based on what his or her own experiences, abilities, interests, and so forth bring to his or her interactions with information. But the process of learning as part of a group, particularly an ICT-enabled one, is certainly quite different from the process of learning on one’s own. In such groups, where is the intersection between individual learning and group knowledge construction? How does each contribute to the other? How do the new affordances inherent in today’s and tomorrow’s ICTs enhance and constrain the outcomes as well as the processes of learning? The questions are sure to outpace the answers for years to come. At bottom, however, learning in any ICT-enabled environment will always depend on learners’ abilities to access, evaluate, and use information of all kinds, in all formats, and at all levels of Anderson and Krathwohl’s (2001) Taxonomy. Whether today’s Internetand Web-based systems remain the penultimate information rich-environment or whether new “penultimate” environments emerge, learning with information will continue to be at the heart of the learning process they support.

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Barab, S. A., & Duffy, T. (2000). Architecting participatory learning environments. In D. H. Jonassen & S. Land (Eds.), Theoretical foundations of learning environments. (pp. 25–55). Hillsdale, NJ: Erlbaum. Barab, S. A., Kling, R., & Gray, J. (Eds.) (2004). Designing for virtual communities in the service of learning. Cambridge, MA: Cambridge University Press. Belland, B., Glazewski, K. D., & Richardson, J. C. (2008). A scaffolding framework to support the construction of evidence-based arguments among middle school students. Educational Technology Research and Development, 56(4), 401–422. Bos, N., & Shami, N. S. (2006). Adapting a face-to-face role-playing simulation for online play. Educational Technology Research and Development, 54(5), 493–521. Bruckman, A. (2006). Learning in online communities. In R. K. Sawyer (Ed.). The Cambridge handbook of the learning sciences (pp. 461–472). Cambridge, MA: Cambridge University Press. Buettner, D., & Mason, D. (1996). MayaQuest: Interactive expedition. Minneapolis, MN: Onion Press. Cannon-Bowers, J. A., & Bowers, C. A. (2008). Synthetic learning environments. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed.). (pp. 317–327). Mahwah, NJ: Lawrence Erlbaum. Chen, F-C., & Wang, T. W. (2009). Social conversation and effective discussion in online group learning. Educational Technology Research and Development, 57(5), 587–612. Cohen, E. G. (1984). Talking and working together: Status, interaction, and learning. In P. Peterson, L. C. Wilkinson, & M. Hallinan (Eds.), The social context of instruction: Group organization and group processes. (pp. 171–177). New York: Academic Press. Dede, C. (2009). Technologies that facilitate generating knowledge and possibly wisdom. Educational Researcher, 38(4), 260–263. Halbert, M. (Personal communication, August 13, 2009). Hanze, M., & Berger, R. (2007). Cooperative learning, motivational effects, and student characteristics: An experimental study comparing cooperative learning and direct instruction in 12th grade physics classes. Learning and Instruction, 17, 29–41. Hew, K. F. (2009). Use of audio podcast in K-12 and higher education: A review of research topics and methodologies. Educational Technology Research and Development, 57(3), 333–357. Hill, J. R., Wiley, D., Nelson, L. M., & Han, S. (2004). Exploring research on Internet-based learning: From infrastructure to interactions. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 433–460). Mahwah, NJ: Lawrence Erlbaum. Howard, C., Boettcher, J. V., Justice, L., Schenk, K., Rogers, P., & Berg, G. A. (Eds.) (2005). Encyclopedia of distance learning. Hershey, PA: Idea Group. Hung, W., Jonassen, D. H., & Liu R. (2008). Problem-based learning. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll, (Eds.). Handbook of research on educational communications and technology (3rd ed.). (pp. 485–506). Mahwah, NJ: Lawrence Erlbaum. Jacobson, M. J. (2008). A design framework for educational hypermedia systems. Educational Technology Research and Development, 56(1), 5–28. Jacobson, M. J., & Azevedo, R. (2008). Advances in scaffolding learning with hypertext and hypermedia: Theoretical, empirical, and design issues. Educational Technology Research and Development, 56(1), 1–3. Jonassen, D. H., Peck, K. L., & Wilson, B. G. (1999). Learning with technology: A constructivist perspective. Upper Saddle River, NJ: Prentice-Hall. Kozma, R. B. (1991). Learning with media. Review of Educational Research, 61, 179–211. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge, MA: Cambridge University Press. Marchionini, G. (1995). Information seeking in electronic environments. Cambridge, MA: Cambridge University Press. McLellan, H. (2004). Virtual realities. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed.). (pp. 461–497). Mahwah, NJ: Lawrence Erlbaum. New Media Consortium and EDUCAUSE Learning Institute (2010). The Horizon report: 2010 edition. Austin, TX: New Media Consortium.

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Chapter 4

Today’s Learners and Learning with Information

Abstract Contemporary information-rich environments, especially the Internet / Web, have changed the way we look at learning. While learning itself remains the same—the construction of personal meaning from interactions with information— the routes to learning have expanded and diversified. So, too, have the challenges. This chapter paints a picture of today’s learners-with-information and delineates the concepts, strategies, and skills that all of us, as learners, need to master to make the most of the learning opportunities that surround us. It surveys relevant research and theory from information studies and instructional design and development to suggest how collaborative research and development across these fields can lead to improved environments for both informational and instructional uses. Drawing on the rich traditions from both fields and on current initiatives that highlight the importance of using information as a tool for learning, the chapter interweaves key traditional and contemporary ideas to offer a slate of possible theoretical frameworks for guiding research in several important areas related to learning in today’s information-rich environments. Popular wisdom tells us that today’s learners—at least the younger ones—are different from their predecessors: they are creative and collaborative, intuitive and interconnected, action-oriented and problem-solving. They are “digital natives” who understand technology almost from birth. They are idealistic yet self-involved, optimistic yet cynical, visually sophisticated yet verbally stunted. They have strong feelings of entitlement—and they value feelings more than thoughts. They multitask with considerable success, and they shift their attention rapidly and flexibly. Like countless generations before them, they have developed skills that are essential in the world they inhabit but that confound many of their elders. And, like those earlier generations, they have spawned worries as well as admiration in those elders—who are concerned about young people’s apparently limited abilities to engage in deep analysis, to think reflectively, and to write coherent sentences (Brown 2000; Dede 2005; Greenfield 2009; Howe and Strauss 2000; Oblinger and Oblinger 2005; Prensky 2001a, b). Recent research has begun to question claims about the vaunted cyberlearning abilities of today’s students: see, for example, Reeves and Oh (2008) for a research review


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4 Today’s Learners and Learning with Information

that culminates in skepticism, and Combes (2009), Head and Eisenberg (2010), and McClure and Clink (2009) for discussions of students’ difficulties in conducting research with digital resources. Nevertheless, there is no doubt that the venues in which students learn and the skills they need to flourish in those venues have changed dramatically in the last twenty years. (See Jenkins et al. 2004 and Ito et al. 2008 for reports on the educational implications of these venues from the MacArthur Foundation, which tracks such developments as they develop.) Even today’s older learners— “digital immigrants”—are confronted by learning challenges and opportunities that differ from those faced by learners in earlier generations: they (we?) often form (or confirm) political views primarily from radio and television rather than from newspapers; build networks of knowledge through social media; and learn how to knit, play the guitar, or even fly a plane by watching YouTube (Baker 2010). In terms of the focus of this book, we might say that all of today’s learners have been thrust into an ocean of information types, sources, and formats that threaten to swamp us in unprecedented ways as we try to use information to make sense of our world. This information is ubiquitous, discrete, ever-changing, and often unvetted. Its quality and authority vary to wild extremes, from the deliberately misleading to the deeply scholarly. Information professionals and media critics are the first to admit that credible and reliable information is often difficult to find today. Educators charged with helping learners to find information, to evaluate it critically, and to use it creatively to construct meaningful personal knowledge are the first to admit that such information competence is difficult to foster. What, then, are the concepts, strategies, and skills that all of us, as learners, must have in order to flourish in today’s information-rich environments? What approaches carry over from earlier research, theory, and practice? And what new ones must we develop? What do we know, and what must we still learn, about supporting today’s learners in a variety of information-rich environments, particularly in the comprehensive and complex one of the Internet / Web? Answers to these questions are suggested by several research threads as well as by the multitude of ideas that have arisen from practice. Chaps. 2 and 3 provide a detailed overview, primarily from the instructional-design literature, of the ways in which learning is supported by the affordances inherent in all the information-rich environments available to us today. Extrapolating from those affordances leads to a wealth of concepts and strategies for helping learners thrive in today’s environments as well as in their simpler antecedents. Information studies, too, has insights to contribute to this discussion, as the following section of this chapter suggests. Together, ideas gleaned from research, theory, and practice in both fields combine to offer a suite of possibilities for supporting learners in today’s multifaceted information-rich environments.

4.1

Research from Information Studies

Not surprisingly, it is the field of information studies—not that of instructional design and development—that led the way in trying to understand the relationship between information use and learning: after all, “information” and all its attributes

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and uses are the overriding concern of this field, while instructional design and development sees information from a more targeted perspective—one that focuses on types of knowledge rather than on “information” as a concept in itself. And although work on information and learning comprises only a small part of the overall information-studies research canon, its longevity and persistence testify to a consistent, if scattered, interest in the topic. For over forty years, the research literature in information studies in general—and on school library media programs in particular—has at least nodded at the link between information and learning. Gaver (1963) was one of the first to raise the issue, and Didier’s (1985) article summarized thirty-eight studies conducted before 1982 that looked at correlations between school library programs and student achievement, broadly defined. Most of the studies Didier reviewed focused on students’ mastery of the concepts and skills they would need to conduct research—that is, to engage in the kind of information-based learning that is the focus of this book. Mancall et al. (1986), writing primarily for practicing library media specialists, created the anthem for looking at the increasingly recognized link between information seeking and learning in the title of their classic and influential article on the role of the school library media specialist at the dawn of the information age: “Educating Students to Think.” Wozny (1982) was among the first to address the issue in an electronic environment, drawing attention to the potential of such environments not only “to provide a new opportunity for assisting students in developing search strategies” (p. 42) but also “to introduce students to a broader world of information” (p. 40) to which they could apply those strategies. Others followed Wozny’s lead—for example, Aversa and Mancall 1986; Callison and Daniels 1988; Crane and Markowitz 1994; Lathrop 1989; Mancall 1984; Neuman, 1993, 1995a, b. All these researchers looked at one or more aspects of the relationship between learning and using electronic resources. Words and phrases like “logic,” “critical thinking,” decision making,” and “higherorder thinking skills” are threaded throughout their work. At the same time, Marchionini’s ongoing focus on students’ “mental models” as they used early electronic resources (Marchionini and Teague 1987; Marchionini 1989; Liebscher and Marchionini 1988) related information use directly to cognitive theory, as did Solomon’s (1994) conclusion that cognitively complex questions pushed students to create more complex strategies to find information successfully. Bilal (2000, 2001) linked seventh graders’ information-seeking processes to their Piagetian developmental stages, while Kafai and Bates (1997) linked elementary students’ Web searching to their development of information literacy. Fidel et al.’s (1999) study of high school students led to concerns that “the introduction of the Internet into schools … may even help some students to develop unproductive learning habits” (p. 34). Neuman (2001a, b), Chung (2003), and Chung and Neuman (2007) have linked information seeking and use directly to the levels of learning in Bloom’s (1956) Taxonomy and, more recently, to Anderson and Krathwohl’s (2001) revision of that classic work. Large et al.’s many studies offer the most intensive and extensive look at information use and learning from an information-studies perspective—examining the relationship of comprehension to such information types as text, animation, and captions (Large and Beheshti 2000; Large et al. 1994a, 1994b, 1995, 1996, 1998, 2002).

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Paralleling this work, school library media researchers who focus specifically on the cognitive dimensions of the research process have repeatedly underscored the basic link between information use and learning in a variety of environments, electronic and otherwise. McGregor, for example, has done a series of studies tracing the higher-order thinking skills that students bring to bear as they look for information for school-based projects—linking their work to all levels of Bloom’s original (1956) Taxonomy (McGregor 1994a, b; McGregor and Streitenberger 1998; Williamson et al. 2007). Kuhlthau (1993) argued that both learning and information seeking are constructivist processes and that “information seeking in libraries [should be] placed in the larger context of learning” (p. 14). Pitts (1994) linked students’ information use to three other “learning strands” (life skills, subject matter, and video production) and concluded that the students’ “limited mental models” related to all four strands conspired with other factors at the research site to limit their success. Todd (1995, 1999) looked at the relationship of information seeking and learning in several studies in his native Australia and—with colleagues Carol Gordon and Carol Kuhlthau at the Center for the International Scholarship in School Librarianship—continues to examine the relationship of information seeking and learning in the United States (see http://cissl.scils.rutgers.edu/). Throughout the 1990s and into the twenty-first century, researchers and theorists associated with the resource-based learning movement have also sought to document the benefits of a kind of learning that is grounded in students’ direct use of information—that is, in their use of original sources and reference materials to answer self-generated questions. (See, for example, Eisenberg and Small 1995; Hannafin and Hill 2008; Hill and Hannafin 2001; Meyer and Newton 1992; Ray 1994). Neuman’s (2004) observation about these researchers’ assumptions—“(1) that students’ personal questions are more important than teachers’ packaged assignments and (2) that information is a more valuable tool for learning than textbooks and other traditional learning tools” (p. 508)—points out contributions from the information-studies perspective as well as the value of seeing information itself as a basic tool for learning. All these efforts—looking at general connections between information use and learning, investigating the details of those connections, and examining how the connections are made (or not made) specifically in interactive information environments—provide a deep and rich conceptual background for studying today’s students’ and others’ use of information as a tool for learning.

4.2

The “Information Literacy” Movement

The emergence of the so-called “digital library” in the late 1980s and early1990s offered a new and exciting venue for studying the relationship between information use and learning. The concept of information literacy—the ability to access, evaluate, and use information for a variety of purposes—took hold in library circles and began to exert tremendous influence on the everyday work of both school and academic librarians. Behrens (1994) chronicles the emergence of this phenomenon,

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noting the importance of the final report of the American Library Association’s Presidential Committee on Information Literacy, issued in 1989: To be information literate, a person must be able to recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information. . . . Ultimately, information literate people are those who have learned how to learn. They know how to learn because they know how knowledge is organized, how to find information, and how to use information in such a way that others can learn from them. They are people prepared for lifelong learning because they can always find the information needed for any task or decision at hand. (ALA 1989)

This definition is especially relevant to the focus of this book because it makes explicit the link between information use and learning. It specifies the higher-order thinking skills associated with effective information use, states the importance to information literacy of knowing “how knowledge is organized,” and stipulates that preparation “for lifelong learning” is the primary goal of information literacy. Its integration of concepts inherent to learning with those essential to information use suggests a theoretical framework that anchors ideas from both information studies and instructional design (Neuman 1997). Specific outcomes related to learning with information were codified for K-12 students in the “Information Literacy Standards for Student Learning,” which appeared in Information Power: Building Partnerships for Learning (American Association of School Librarians and Association for Educational Communications and Technology 1998), and for postsecondary students in a series of publications of the Association of College and Research Libraries: Information Literacy Competency Standards for Higher Education (2000), the basic document, was followed by Information Literacy Standards for Science and Technology (2006), Research Competency Guidelines for Literatures in English (2007), and Information Literacy Standards for Anthropology and Sociology Students (2008). In addition, practical guidelines for helping students learn with information appeared across the K-postsecondary spectrum. (See, for example, Thomas 2004; Grassian and Kaplowitz 2001, 2009). Today, research into the nature of information literacy and ways for addressing it continues to move forward: the Information Literacy Project housed at the Information School of the University of Washington, for example, conducts research on “information problem-solving; programs in schools, libraries, and the general society; and the nature and impact of information literacy across audiences and contexts” (http://ischool.uw.edu/research/topics/info_literacy.aspx). Clearly, information and learning remain closely linked among the interests, theories, and practices of the nation’s information professionals.

4.3

Learning with Information Today

Today’s information environment has changed radically from that of the early digital library. Primarily text-based and technologically limited, early digital information environments have evolved into the full-blown, highly visual, and highly interactive

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information-rich environment of the Internet / Web. The documents referenced above, created largely in response to the emergence of this earlier environment, catalog scores of specific concepts and strategies students can master to undergird their learning in more sophisticated environments as well. Other publications stand beside these “standards” documents, particularly recent documents created for K-12 students, and expand their reach. The National Educational Technology Standards for Students (International Society for Technology in Education 2007), for example, includes a set of “Research and Information Fluency” skills among its six categories and embeds a number of additional information skills under headings like “Creativity and Innovation,” “Communication and Collaboration,” “Critical Thinking, Problem Solving, and Decision Making,” and “Digital Citizenship.” Although the focus of the ISTE Standards is the use of technology in relation to all these areas, the statements themselves assume that information is the raw material on which the technology operates. Similarly, the Standards for the 21st-Century Learner (American Association of School Librarians 2007) suggests that information literacy is the basis for student learning in each of this document’s four categories: “Inquire, think critically, and gain knowledge”; “Draw conclusions, make informed decisions, apply knowledge to new situations, and create new knowledge”; “Share knowledge and participate ethically and productively as members of our democratic society”; and “Pursue personal and aesthetic growth.” The AASL Standards go on to specify not only the skills but the “dispositions” (e.g., initiative, flexibility, persistence, etc.) students need to develop in order to flourish as twenty-first-century learners. (See Small and Gluck 1994 for their discussion of motivation, a “disposition” they identified some years ago). At first glance, integrating the knowledge and skills cataloged in all these lists into an all-inclusive whole would seem to provide an avenue for creating a comprehensive taxonomy of what learners need to know and be able to do to learn with information. Yet both practical and conceptual factors stand in the way of such an exercise. Practically, of course, the resulting pages and pages of statements would simply be too cumbersome to be useful. Conceptually, a range of difficulties would limit its utility. For example, the statements in such a list would range widely across their levels of granularity. Consider, for example, these two statements: The information literate student evaluates information and its sources critically and incorporates selected information into his or her knowledge base and value system (ACRL Competency Standard 3) Evaluate information found in selected sources on the basis of accuracy, validity, appropriateness for needs, importance, and social and cultural context (AASL Inquire, Think Critically, and Gain Knowledge, Skill 1.1.5)

The statements obviously describe similar outcomes, but one was devised for postsecondary students and one for a K-12 audience. Both the orientations of the statements and their varying levels of detail reflect the needs of those particular audiences and suggest how difficult it would be to develop a composite that spoke effectively to all those needs.

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Further, such a list of standards could never convey the nuances of each s tatement as it applies in different contexts and even in similar ones. Consider these statements: Advocate and practice safe, legal, and responsible use of information and technology (ISTE NETS for Students, Digital Citizenship, Indicator a) Use information and technology ethically and responsibly (AASL Share Knowledge and Participate Ethically and Productively as Members of Our Democratic Society, Skill 3.1.6)

Written for a K-12 context, both statements call for the ethical use of technology. But the statement from a “technology” organization with an international focus calls for the advocacy of such use across boundaries, while that from the “library” organization with a national focus omits the call for technology advocacy and speaks specifically to the context of the United States. Negotiating a statement that could address both perspectives would be difficult at best. Perhaps most importantly, a list based on current ideas would necessarily exclude knowledge and skills that have not yet been identified or whose importance to learning with information is little understood. Learning with information is as broad as a concept can be, and the specific knowledge and skills that are germane to this general notion are still emerging. The ways in which the learning affordances noted in Chaps. 2 and 3 can be considered specifically when learning with today’s information media are rarely mentioned; details about the learning described in the “standards” statements are sparse at best. Other ideas have not yet been thoroughly researched—for example, Perzylo and Oliver’s (1992) and Small and Ferreira’s (1994) findings related to children’s limited abilities to extract and report visual information presented in multimedia databases and Neuman’s (2001a, b) identification of the critical importance of helping learners understand how to structure information in an interactive information environment.

4.4

Directions for the Future

Several groups and organizations that are concerned with learning as a whole rather than primarily with the contributions to learning made by individual disciplines have emerged in recent years and suggest a way forward. The Partnership for 21st Century Skills, founded in 2003; work from the Educational Testing Service dating back to 2003; and the Microsoft/Cisco/Intel Partners in Education Transformation Project, founded in 2009, all offer intriguing possibilities. Arguably the most influential of these groups is the Partnership for 21st Century Skills, which is supported by some forty organizational “partners” ranging from professional academic organizations to publishers and commercial vendors. Its landmark publication Framework for 21st Century Learning (2004) specifically suggests a holistic view that offers “a unified, collective vision for 21st century learning that will strengthen American education” (www.21stcenturyskills.org)

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across the board. The document obviously influenced the AASL Standards released in 2007—the American Association of School Librarians is one of the partners— and its ideas generally parallel those in the NETS Standards and in the efforts of the Educational Testing Service and the Microsoft/Cisco/Intel project. The Framework includes eleven “core subjects” (traditional curricular categories like science and language arts) and four “21st century themes,” including such topics as global awareness and civic literacy. Most significantly for learning with information, the document offers three sets of skills that support students’ mastery of those core subjects and contemporary themes: “learning and innovation skills,” “life and career skills,” and “information, media, and technology skills.” The Framework’s marriage of “information” skills and “media and technology skills” bridges ideas inherent in earlier sets of information-technology and information-literacy standards noted above. And through its identification of “information, media, and technology skills” as necessary for mastering its fifteen subjects and themes, the Framework also moves learning with information into a key position in its “holistic view.” While the Partnership deals primarily with a K-12 audience, the Educational Testing Service (ETS) addresses primarily postsecondary students. Its first white paper on the topic, released in 2003, is entitled Succeeding in the 21st Century: What Higher Education Must Do to Address the Gap in Information and Communication Technology Proficiencies (www.ets.org/portal/site/ets). Like the Framework, this document offers a welcome new focus to those concerned with learning in information-rich environments because it, too, breaks with the 1990s focus on subject-area standards to focus on establishing cross-disciplinary standards related to what ETS calls “ICT literacy”— that is, literacy related to the use of information and communication technologies. ICT literacy, as defined by ETS, clearly puts information at the core of learning: [ICT literacy is] the ability to use digital technology, communication tools, and/or networks appropriately to solve information problems in order to function in an information society. This includes the ability to use technology as a tool to research, organize, evaluate and communicate information and the possession of a fundamental understanding of the ethical/legal issues surrounding the access and use of information. (Educational Testing Service 2003, p. 11)

ETS describes seven components of ICT literacy that illustrate this crossdisciplinary focus and that are grounded in the assumption that information is our basic tool for learning. The components include defining an information need, accessing information, managing information, integrating information from multiple sources, evaluating information, creating new information, and communicating information. Information professionals would not be surprised by this list, since it describes essentially the same competencies that are embedded in the ACRL and AASL/AECT standards documents and that undergird information-literacy instruction in school, public, and academic libraries across the country. In 2009, the Microsoft/Cisco/Intel Partners in Education Transformation Project (http://www.microsoft.com/education/programs/transformation.mspx) stepped into the ongoing discussion about twenty-first-century learning skills. Not surprisingly, the concepts and language of the Framework are also embedded in the work of this group: Microsoft, Cisco, and Intel are all among the “member organizations” that

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support the Partnership. What is innovative about this initiative, however, is that a number of its own partner organizations are international in scope, suggesting that the focus on the skills touted by the Partnership will not be limited to North America. With a project director housed at the University of Melbourne, the project has already established an international presence. Taken together, these three initiatives provide a picture of the general concepts, strategies, and skills that learners need to master in order to use information as a tool for learning. Although none of the three arose within the field of information studies, each clearly calls for a kind of learning that draws on core elements of this field that have been its primary focus for over twenty years. “Catching up to” information literacy as delineated in the 1980s, the initiatives provide direction and an overarching structure that could indeed guide research and practice in identifying the specific concepts, strategies, and skills that will enable learners to learn in information-rich environments of every kind both now and in the future. Within the framework of this general structure, researchers and practitioners in both information studies and instructional design and development—as well as in other areas— can fill in the gaps related to using information as a tool for learning.

4.5

Filling in the Gaps

Information specialists, instructional designers, and other educators are not bereft of the knowledge and skills required to help fill those gaps: as Chaps. 2 and 3 and the beginning of this chapter have chronicled, research and theory relating to learning with information have been pursued for almost a century. The learning affordances noted throughout Chaps. 2 and 3 suggest myriad opportunities to identify the concepts, skills, and strategies today’s learners need to profit from contemporary information-rich environments. The work of the information-studies community, especially in regard to information literacy, suggests avenues to learning that can be adapted to all of those environments as well. Much of this work has been “siloed,” however, with experts in both fields and their various subfields drawing on their own literatures, studying their own slices of phenomena, and building their own disciplinary perspectives. Traditionally, there has been little crossover from any one academic field to another, and in the social sciences “new” questions are often asked with little attention to work in adjacent disciplines that might shed light on them. Several notable cases in point are outlined in Chaps. 2 and 3: parallel research thrusts in film, in gaming, and in distance learning have all discovered important concepts; but researchers seldom address or build on “outside” concepts in their own work or acknowledge them in their literatures. The contemporary focus on “twenty-first-century skills” and today’s trend toward holistic approaches to learning, however, not only call for but require researchers across disciplines to pool their understandings in order to consolidate our knowledge and to ask questions that will move that knowledge forward rather than rediscovering old ideas as they apply in new environments. And by focusing

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specifically on the concept of learning with information—that is, on helping learners understand how information itself is represented, organized, and presented rather than only on the containers or mechanisms that carry that information or the strategies for locating and accessing it—the fields of instructional design and development and information studies can come together to build on existing understandings to generate the new ideas that will help today’s learners become adept at the kind of learning our contemporary information-rich environment requires.

4.6

he Contributions of Instructional Design T and Development

Within the overall framework provided by the three initiatives noted above, an instructional-design professional might start with a consideration of the kinds of information objects described in Chaps. 2 and 3—single-sense, multisensory, and interactive. By looking at the learning affordances associated with each kind and extrapolating from them to the concepts and skills needed to capitalize on them, we can begin to identify what learners need in order to learn with various representations of information. Building on this basic identification, we can begin to piece together a comprehensive understanding of what today’s learners need to know and be able to do for successful receptive and expressive learning in the full range of information-rich environments. Even apparently simple information objects have their own “grammars” and conventions; understanding how to “read” those grammars and conventions is the first step in learning with any information object— simple or sophisticated—that embodies them. Learning with single-sense objects, for example, requires “visual” and “auditory” literacy whether these objects occur as stand-alone items or as components of more sophisticated interactive ones. According to Smaldino et al. (2008), visual literacy is “the learned ability to interpret visual messages accurately and to create such materials” (p. 374). Whether those messages involve simple graphics formats like the ubiquitous food-pyramid poster or sophisticated presentations created with Photoshop, learners must have some knowledge of basic graphics principles related to line, shape, color, etc., in order to understand what they are designed to communicate. In order to create visual messages that communicate accurately, completely, and efficiently, learners need an even deeper understanding of those principles: they must understand why a red “X” is better than a green one for marking a condemned building on a map or why they should use a pie chart to convey proportions but a line graph to convey trends. Similarly, learners trying to understand the latest news—whether listening to it over the radio or through a podcast— must be able to extract relevant information from a stream of sound, follow and “outline” an auditory sequence, and understand how music and tone of voice affect meaning. Learners creating oral histories with simple analog tape recorders or advanced digital media must draw on those same abilities to create interesting and effective products of their own.

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Lohr and Gall’s (2008) survey of strategies for creating effective representations provides research-based insights that can be applied to single-sense information objects as well as useful suggestions related to some kinds of multisensory ones. Both static and dynamic multisensory objects embed visual and auditory characteristics like those in single-sense objects and add their own unique grammars and conventions to the mix. The “static” multisensory objects in an information-rich environment like a museum exhibit, for example, create certain effects by building on established visual and auditory conventions in the exhibit’s physical arrangement and in its audioguide: a display that begins with examples of Van Gogh’s somber early paintings and ends with examples of his colorful and exuberant later work creates a visual narrative that supports learners’ understanding of the artist’s development. An auditory narrative that explains that development provides a verbal reinforcement of that understanding. Museum curators must obviously understand principles related to graphics and verbal instruction in order to create successful exhibits, and even the youngest learners creating classroom displays and dioramas need to draw on similar knowledge and skills to be effective. Learning with dynamic multisensory information objects continues this progression: these objects make use of basic visual and auditory characteristics noted above and extend them in ways that are unique to this category and that support particular kinds of learning (Kozma 1991). Elements of Salomon’s (1974) “filmic code,” for example, obviously help to create the particular instructional power of information-rich environments like television and video presentations. To learn the most with dynamic multisensory information objects, learners must understand not only basic visual and auditory characteristics but also such things as how point of view is established by pacing, camera techniques, audio cues, and the juxtaposition of scenes: a video clip of Alaska that uses visuals and music to present the state as a lush habitat for bears and eagles supports one side of the argument over oil exploration, while a clip that focuses on its harsh, uninhabited tundra supports the opposite side. On the evening news, a cut from a battlefield scene to a military hospital establishes a particular perspective on war that is lost in a cut from that battlefield to a military parade. “Media literacy”—the ability to conduct a critical analysis of images and sounds, special effects, and texts that accompany them in multisensory dynamic objects—and to create media products that use these characteristics effectively, accurately, and efficiently—builds on basic visual and auditory ideas. Learners must reach reasonable levels of both “visual literacy” and “auditory literacy” in order to be “media literate” as well. Finally, learning with interactive information objects brings this progression to its culmination: constructing meaning both with stand-alone interactive objects and with those that populate the information-rich environment of the Internet / Web relies on all the basic underlying “literacies” noted above as well as on a mastery of the unique elements of interactive environments. Basic principles of graphic design underlie the creation of both board games and human-computer interfaces, while conventions associated with audio formats and with “motion media” are brought to bear on the creation of the full range of “instructional products” available online. While these environments embed unique characteristics of their own—as

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noted especially in Chap. 3—it is important not to forget that they build on the basic elements described above. Learners trying to make meaning from a simulation of frog dissection must draw on basic visual and auditory concepts and skills as well as on their abilities to click, point, and navigate successfully; when creating their own multimedia products and mashups, learners must draw on similar knowledge in order to design and develop creative and powerful materials that truly incorporate the benefits of interactivity. What we know of the Internet / Web environment suggests that its unique array of information and communication technologies (ICTs) offers myriad additional opportunities for learning, particularly expressive learning, beyond those offered by the discrete information objects described above. In addition to the many affordances inherent in its individual components, the general affordances of the Internet / Web environment as a whole—distributed processing, discourse, and collaboration, separately and in combination, as detailed in Chap. 3—explode the possibilities for learning that earlier technological environments could only suggest. While much must still be learned about these general affordances as well as their general and specific implications, it is already clear that the information-rich environment of the Internet / Web has an unprecedented and powerful role in contemporary lifelong learning. It is also clear that the knowledge and skills required to take advantage of this environment are complex and sophisticated. From participating successfully in simple “electronic penpal” projects, to engaging in collaborative problem-solving and discovery, to using suites of tools to create and disseminate group projects, today’s learners must master both familiar and still-to-be-discovered abilities to thrive in the information age. Instructional-design researchers who situate their work within the larger framework of information use described by the Partnership for 21st Century Skills and the components of ICT literacy delineated by the Educational Testing Service and others can bring decades of knowledge to bear on the design of the contemporary information systems that serve more and more frequently as venues for learning. Similarly, by folding into the details of their designs the information-science knowledge base on representing, organizing, evaluating, and using information—as described below—designers can create richer and deeper instructional systems that take advantage of decades of knowledge from their sister field. Researchers in information studies, of course, would benefit from the active pursuit of instructional-design considerations as they explore the design of improved information systems and their effective use by a wide range of patrons (Neuman 1993). Working together, researchers from both fields could develop systems that could serve more effectively as both informational and instructional venues.

4.7

The Contributions of Information Studies

As stated above, many of the elements of the contemporary framework for learning described here read like a list of topics that have long been the province of information professionals. In both research environments and everyday practice,

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i nformation professionals have developed expertise in the key elements of that framework. They know how information is represented—how audio and visual files as well as text-based ones are created, presented, and stored. They know how information as a whole is organized—not only on bookshelves and in library bins of CDs and DVDs but in databases and other digital collections. They know how information in individual disciplines is organized—and what keywords and other search strategies are most likely to lead to “good” information about specific topics. They know what makes information “good”—relevance, timeliness, credibility, age-appropriateness, and more. They know how to design and use interfaces—and which websites are well-designed and lead users to appropriate information and which lead to conceptual and logistical dead ends. They understand ethical issues related to information use—copyright, fair use, and emerging trends. They are, in fact, society’s premier information experts. By and large, however, the field of information studies has not focused on learning as a goal of information seeking. The literature is replete with “information-seeking models,” but these are for the most part general and focus on information seeking as a global skill—and assume the process is over once appropriate information has been found. The most famous and influential among the models—for example, those of Ellis (1989), Dervin (1992), Kuhlthau (1993), Leckie et al. (1996), T. D. Wilson (1981, 1999), and Spink (1998)—fall into this category. Several models describe information seeking in specific environments (e.g., Marchionini’s 1995 model for information seeking in electronic environments) and on specific topics (e.g., Cogdill’s 2003 model illustrating how nurse practitioners seek health-care information for their patients), but these also focus on accessing the information rather than on using it for particular purposes. Dervin’s model addresses “sense-making,” and Kuhlthau’s was developed in and contextualized by an educational environment, but even these two stop short of making a direct link between information seeking and learning. Wilson’s work offers perhaps the closest connection. In 1996, he extended the typical information-seeking model to an “information behavior” model that includes a step called “information processing and use” [italics added], thus opening a door to thinking about learning as a possible use for information. In 1997, he published an information problem-solving model that uses terminology familiar to educators and instructional designers and that directly connects information seeking to a particular type of learning. While some information-studies researchers have walked through Wilson’s open door (e.g., Gross and Latham 2009), for the most part even glancing attention to learning is rare among the field’s researchers—who tend to leave the “use” part of the information-seeking process to others. The situation in practice is closer to the ideal. Many practicing librarians—and certainly those who work in school and academic settings—have a well-developed sense of the relationship of information design to learning. (See, for example, Asselin and Doiron 2008 Kuhlthau et al. 2007 Stripling and Pitts 1998.) School library media specialists, in fact, are required to blend information use and learning: they are both teachers and librarians, and certification requirements across the United States require them to have academic and practical experience in teaching

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and learning and in the basics of information science. On the academic-library side, ACRL’s series of guidelines provides evidence of the increasing focus on learning by college and research libraries, and ACRL’s information-literacy discussion list has existed since 2002 (http://www.ala.org/ala/mgrps/divs/acrl/about/sections/is/ ilil.cfm). More recently, members of the Blended Librarian Online Learning Community (http://blendedlibrarian.org/) formed a network to focus precisely on strengthening the growing relationship between information literacy and learning in colleges and universities. To information specialists who work in schools, then, it is not surprising that Eisenberg and Berkowitz’s “Big 6” “information problem-solving” model has undergirded K-12 instruction in school libraries and media centers for over twenty years (Eisenberg and Berkowitz 1990; Eisenberg et al. 2004). Blogger and school librarian Joyce Valenza (http://teacherlibrarian.ning.com/profiles/blog/ list?user=joycevalenza) and the array of teacher-librarians whose ideas populate the California School Library Association’s School Library 2.0 blog (http://schoollibrarylearning2.csla.net/) provide a vast array of tools and strategies to help their fellow practitioners focus on learning with information. Valenza’s “Learning Tools Smackdowns” are standard—and popular—fare at practitioners’ conferences at which panels of practicing school librarians share their ideas for using Web 2.0 tools in their instructional programs (see, for example, http://pslasmackdown. wikispaces.com). Overall, however, deeper and more widespread research-based understanding of how the details of information design can contribute to learning would help refine both theory and practice in the field. Just as their counterparts in instructional design and development need to “catch up with” concepts and skills from information science, information professionals need to catch up with instructional-design theory and practice in order to help learners capitalize fully on today’s informationrich environments. They need to attend not only to how information is represented, organized, and presented to enhance access in various collections but also to how varieties of representation, organization, and presentation support and hinder different kinds of learning. In other words, they need to be aware of the cognitively relevant characteristics (Kozma, 1991) of single-sense, multisensory, and interactive information objects so they can help learners build their skills in learning from every type of information object. Consider, for example, that information professionals certainly know at a general level that pictures can often make information more understandable than text alone—just as the rest of us do. But to be optimally helpful to learners, they need to know specifically how the design of visual representations helps and hinders learners trying to extract and create meaning from those visuals. How much detail in a diagram of the circulatory system is useful for grasping how blood flows and how much is confusing—whether that diagram appears in a collection of posters or as a slide on a CD-ROM? Similarly, librarians have a strong sense of how various organizational patterns within collections help and hinder access, but they need to know how to use that knowledge to help learners grasp the underlying structures inherent in various disciplines. How do mammals differ from reptiles, and how

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does understanding that difference affect a learner’s success at finding and understanding information in a biology reference collection—whether that collection is on the shelves or in a database? Information professionals are fully aware that information comes in all shapes and sizes, but they need to understand how information presented at varying levels of difficulty affects learning among learners at varying levels of cognitive development. What details make one database more suitable for a middle schooler encountering Greek mythology for the first time and another a better choice for a classics major at a university? Information professionals can certainly build on the concepts and skills brought to bear by research and practice in library and information science. The field has spent decades studying and advising various kinds of “users” as they seek information. Researchers and practitioners alike have long addressed such topics as question generation, the development and application of search strategies, the evaluation of information and its sources according to various criteria, etc.—as these relate to all ages and stations of information users. Researchers and theorists have pondered how information is organized and represented to meet various user needs, the ways learners at various levels access information, the kinds of information they seek, the reasons they seek it, and their difficulties in locating and evaluating high-quality information. Although these efforts are not generally associated with learning per se—the “use” component of information behavior—ideas about the relationship of information to learning are clearly implicit. (See Agosto and Hughes-Hassell 2010 and Lu 2010 for current examples.) The next step, then, is to forge explicit, research-based links between information seeking and learning—a challenge that is only beginning to be met. While researchers in both information studies and instructional design have addressed these links incidentally, they have rarely done so together or from a shared theoretical perspective. Once again, the vision of information use promulgated by the Partnership for 21st Century Skills, the Educational Testing Service, and the Microsoft/Cisco/Intel Partners in Education Transformation Project provides the bridge: it suggests a conceptual framework that can incorporate the insights and assumptions of both fields in order to move together to advance our understanding of learning in the informationrich environments of the information age. As noted earlier, researchers from both fields could work together profitably to develop systems that could serve effectively as both informational and instructional venues.

4.8

I nformation Studies Meets Instructional Design and Development

It is clear from the work of the three initiatives noted above that contemporary views of learning center on the use of information. The core learning activities promulgated by these efforts all focus on learners’ needs to understand how to find, gather, understand, manage, and create information. These “information skills”

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describe the essence of contemporary learning, and their centrality serves as the focal point for contemporary research in learning and can provide a bridge between information-science and instructional-design research and practice as well.

4.8.1 Research Issues Over the years, a scattering of information-studies researchers have addressed how children use digital materials in schools and have offered suggestions for improving those resources to make them more usable for this audience—and therefore more likely to contribute to their learning. For example, Borgman et al.’s (1995) classic work on children’s use of an online catalog identified a number of design “fixes” that would make searching easier for children as young as nine. A special issue of Library Trends called “Children and the Digital Library” (Jacobson 1997) chronicles a variety of insights about children’s experiences in electronic environments, including the ways these environments contribute to (and hinder) learning. Researchers like those cited earlier in this chapter long ago confirmed children’s abilities to navigate effectively in such environments (Marchionini 1989, Large et al. 1994a) and identified areas that still need attention today. Two of these areas—the challenges students face in using visual information and in creating coherent cognitive structures from the information they find—seem particularly important to learning in today’s world of the Internet / Web. Some twenty years ago, Perzylo and Oliver (1992) found that sixth graders failed to use the highly visual elements of a National Geographic CD-ROM product for their assignment—largely because they lacked the means and strategies for recording and incorporating the nontextual components that carried much of the information they sought. Students preferred to access the sound, video, photographic, and graphics information of the product (in that order) and chose to read only that textual information that was “brief in its extent and … selected intentionally [such as] the photo captions” (p. 237). But to complete their assignment—a traditional written report—students used virtually no information but the textual material and incorporated ideas from other representations of information only through references in their narratives. While the researchers acknowledged that the text-based nature of the assignment and the product’s text-only print capability clearly affected students’ behavior, those issues are largely beside the point today. What remains important is the researchers’ conclusion that students’ performance reflected not only these “logistical” factors but also a key conceptual one: the “students appeared to have no skills or knowledge in seeking and recording information from other than textual sources” (p. 238). Large et al.’s (1994b) observation in a study with roughly the same age group confirms and extends this finding: they found that “in general the multimedia group failed to benefit fully from the dual coding of visual and verbal information” (p. 526)—suggesting that the issue is how well students can construct meaning from the interconnection of text and visuals that is

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characteristic of multisensory information objects. Similarly, Small and Ferreira (1994) found that middle schoolers who used a print resource tended to take written notes and to engage in more “extracting” behaviors than did comparable students who used a multimedia resource and generally took only “mental notes.” Little if any contemporary research seems to have been done on learners’ abilities to use visual information presented in interactive formats, and the question remains about how children and others might learn to mine non-text formats for in-depth information. The strategies that teachers and students must use to optimize visual learning in the Internet / Web environment—whose power stems largely from its ability to provide visual representations—are still to be discovered. Similarly, Neuman’s studies (1993, 2001a, b) of students’ use of electronic information resources have raised an issue that needs further investigation: the place of structure in using information for learning. Variations on this theme emerged in both studies (which, together, involved over 200 high-school and middle-school students). One illustration stems from a group of gifted freshmen, who included items related to organic chemistry in their bibliographies for papers on topics in inorganic chemistry. Their lack of awareness of the fundamental organizational structure of chemistry not only led them to make searching and writing errors, it also suggests a corresponding lack of understanding about how to construct cohesive and reasonably accurate mental models of chemical knowledge. Today’s information-rich environments—particularly the Internet / Web, the ultimate enabler of clicking, browsing, and skimming from one resource to another— generally come without the familiar tables of contents, chapter headings, and indexes that indicate the overall structure of “traditional” documents. In most cases, learners must impose their own structures on the information they glean as they surf from one resource to another. But that might not be so easy. Another illustration of the problem of structure comes from a group of seventh graders assigned to develop a five-paragraph essay on an animal of their choice. Instructed (and guided) to find their information only on the Internet / Web, the students were almost entirely dependent upon a four-page teacher-generated template—actually a teacher-imposed structure—that they used four times during the assignment. First, they used it to guide their note taking so they focused specifically on the information the final essay required (diet, habitat, predators, etc.) and recorded that information in the required overall structure (introduction, three explanatory paragraphs, conclusion). Next, they used a similar template as a “graphic organizer” that specified the order in which the information was to appear within each paragraph (topic sentence followed by either two or three explanatory sentences); a third time, they used the template to create their rough drafts; finally, they used it as the form on which they submitted their final papers. The amount of structure the teacher thought it necessary to provide in order to enable students to write a simple, five-paragraph paper is notable. It is also notable that all three of the other teachers as well as the library media specialist in this study used similar templates and otherwise provided extensive support to enable students to structure their information into final products. Students were never taught “structure” as a concept of its own, and even good students were unable to articulate how

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they put ideas together to create a whole. The question, then, becomes, What will happen to students, especially less-advanced ones, when there is no one present to suggest or impose a structure on their encounters with the vast array of information available in today’s information-rich environments? How many students are in fact capable of inventing and imposing their own patterns? And are the personal structures they erect relatively complete and coherent or are they built on scattered factoids arranged haphazardly? While products like Inspiration and Kidspiration (http://www.inspiration.com/) are powerful tools for helping users organize and structure information, how will unsophisticated learners without the software at hand be able to create reasonable mental models from the information they encounter? Ultimately, the question of structuring information independently can be a critical one. Will a teenager be able to construct a reasonable mental model of job prospects and educational requirements to select a satisfying career? Will a young parent be able to construct one of health-care information from a variety of sources—even reputable ones—to help him or her make good decisions about a sick child? Will someone nearing the end of a career be able to construct one of financial matters that will ensure a comfortable retirement? These questions offer a powerful illustration of the intersection between information use and learning at the highest level of Anderson and Krathwohl’s (2001) “Cognitive Process Dimension” (see Fig 1.2). “Structuring”—that is, creating a personal mental model—appears as “create” in this figure, where it is defined as the ability to “put elements together to form a coherent or functional whole; reorganize information elements into a new pattern or structure” (p. 68). Creation, in this sense, doesn’t involve writing a Booker-prize winning novel or discovering the solution to Fermat’s theorem. But it does involve putting together ideas—facts, concepts, rules, procedures, and strategies—in new ways to solve problems and make one’s way in the world. This kind of “create” assumes the development of a reasonably accurate and coherent personal structure, and it describes a type of learning that is especially well supported by today’s information-rich environments. The tools embedded in the Internet / Web not only support the creation and dissemination of new knowledge—they require it. Unless learners can create meaningful structures from Internet / Web information, their ability to achieve learning at this highest level will be compromised. While instructional-design research in using information for learning has lagged behind the focus on this area in information studies, some key works exist. For example, Lee et al. (2008) summarize years of research on “generative learning,” which employs similar constructs. In a welcome development, several instructionaldesign theorists and researchers have begun to work directly at the intersection of information use and learning. Cromley and Azevedo (2009), for example, have interwoven work in both information design and instructional design to contextualize their study of how students locate information within digital resources; Hannafin et al. (2009) have looked at cognitive aspects of Web-based learning with some attention to the problem of “information overload”; Lim and Tay (2003) have asked how different types of ICTs foster higher-level thinking. This research strand is just

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beginning in the instructional-design community, and its emergence is encouraging and important. As noted earlier, linking this research—and its researchers—to their counterparts in information studies could blend the best of both traditions into an even more powerful research model.

4.8.2 Theoretical Frameworks Blending research across the fields would, of course, require an appropriate theoretical framework. One such framework is suggested by the key concepts shared by the Partnership for 21st Century Skills and the Educational Testing Service. Drawing from the Partnership’s “information media and technology skills” and ETS’s seven components of ICT literacy could lead to a comprehensive framework that researchers from both fields—especially in collaboration—could use to guide the next generation of inquiry. Augmenting this general framework as appropriate with ideas embedded in such documents as the standards promulgated by AASL, AASL/ AECT, ACRL, and ISTE could allow researchers to contextualize their work to address the specific needs of individual constituent groups. Another possible theoretical framework stems from the work of David Jonassen, who has led the way in posing theories to the instructional-design world on the relationship of technology to constructivism and, indirectly, to the relationship of information and learning. His 1996 book Computers as Mindtools for Schools: Engaging Critical Thinking was a breakthrough for both researchers and practitioners struggling to understand how to use then-emerging Internet / Web technologies to support meaningful learning. His later work with two other well-respected instructional-design theorists created a robust conceptual framework for the kind of research that can blend instructional-design and information-science thinking to address the kinds of learning envisioned by today’s focus on ICTs. Their identification of the five roles that these technologies might play in supporting learning and the ways in which this support manifests itself offer a cafeteria of research questions that are essentially about learning in information-rich environments. Their scheme offers descriptions of technology as: tools to support knowledge construction: for representing learners’ ideas, understandings, and beliefs for producing organized, multimedia knowledge bases by learners information vehicles for exploring knowledge to support learning-by-constructing: for accessing needed information for comparing perspectives, beliefs, and world views context to support learning-by-doing: for representing and simulating meaningful real-world problems, situations, and contexts for representing beliefs, perspectives, arguments, and stories of others for defining a safe, controllable problem space for student thinking a social medium to support learning by conversing: for collaborating with others for discussing, arguing, and building consensus among members of a community for supporting discourse among knowledge-building communities

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4 Today’s Learners and Learning with Information an intellectual partner to support learning-by-reflecting: for helping learners to articulate and represent what they know for [supporting them in] reflecting on what they have learned and how they came to know it for supporting learners’ internal negotiations and meaning making for constructing personal representations of meaning for supporting mindful thinking (Jonassen et al. 1999, pp. 13–14)

A third approach is suggested by the I-LEARN model presented in Chap. 5 of this book. Grounded in theory, research, and practice from both information studies and instructional design, the model suggests a research structure that consciously bridges the gap between information use and meaningful learning. Its six categories (Identify, Locate, Evaluate, Apply, Reflect, and kNow) describe the overall process of seeking and using information for learning, and its final three categories in particular establish I-LEARN as an extension of information-seeking into the learning arena. Like Jonassen et al.’s list of technologies’ roles in constructivist learning, I-LEARN suggests a full range of factors for instructional designers and information specialists to consider as they study approaches and develop materials that will help today’s learners become adept at the kind of learning our information-rich environments require.

4.9

Conclusion

Whether young or old, novice or expert, today’s learners must master a wide array of unfamiliar concepts, strategies, and skills to learn efficiently and effectively from an even wider array of information objects. Decades of research and practice in both information studies and instructional design have yielded a strong foundation for understanding individual components of this kind of learning, but the contributions of the separate fields fall short of the holistic vision of learning with information that undergirds discussions of learning today. Linking the research, theory, and practice from both fields could create a powerful foundation for understanding how to foster higher-level learning in today’s information-rich environments and for helping more students achieve it. Such learning is the ultimate goal of such initiatives as the Partnership for 21st Century Skills and the ICT-literacy focus of the Educational Testing Service, and we are just beginning to understand it. It is difficult to overstate the importance of increasing our understanding of how to help learners thrive in information-rich environments. According to the Kaiser Family Foundation, today’s eight- to eighteen-year-olds spend an average of 7 hours and 38 minutes each day using “entertainment media” like television (4 hours, 29 minutes); music/audio (2 hours, 31 minutes); computers (1 hour, 29 minutes outside of school work); video games (1 hour, 13 minutes); print (38 minutes, also outside of school work); and movies (25 minutes) (Rideout et al.

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2010). And while the report is clear that the study’s focus is on the informationrich world of entertainment media rather than on the use of media for learning, the authors also note that these media “[offer] a constant stream of messages about families, peers, relationships, gender roles, sex, violence, food values, clothes and an abundance of other topics too long to list” (p. 1). These are topics about which these “Generation M2” children must learn a great deal in their formative years—and they are learning about them from the entertainment media. Whether they are skilled enough to learn accurate and actionable information from such sources is a very open question. In terms of using today’s media specifically for learning, the report notes that about a third (33 percent) of Generation M2 children report “using a computer for school-related work in a typical day,” averaging 16 minutes for this activity (p. 23). Thirty-one percent of respondents in grades seven through twelve say they use media most of the time while they’re doing their homework, while 25 percent say they use it “some of the time” (p. 76). Further, “nearly half (47%) of all heavy media users [i.e., those who consume more than 16 hours of media content on a typical day] say they usually get fair or poor grades (mostly C’s or lower), compared to 23% of light media users [i.e., those who consume less than three hours]” (p. 4). The negative correlation between “media” use and learning—or at least between media use and grades—raises questions about how to embed the learning affordances of the full range of formats into the types of media to which today’s potential learners are drawn. Figures like those in the Kaiser Family Foundation report clearly suggest both opportunity and necessity: today’s information-rich environments are almost irresistible, and helping learners use them for their own short- and long-term benefit is a growing imperative. Today, we are just at the beginning of understanding how to exploit these environments as learning venues. As the venues themselves continue to grow and change, our theory, research, and practice must adapt accordingly. Combining the insights of both information science and instructional design in a comprehensive research agenda would be a powerful strategy for achieving the knowledge we need to maximize the promise for learning inherent in the richest of the information-rich environments. Guided by a broad and flexible conceptual structure—like Jonassen et al.’s (1999) themes or the I-LEARN model—such a strategy could yield compelling and useful results.

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Jonassen, D. H., Peck, K. L., & Wilson, B. G. (1999). Learning with technology: A constructivist perspective. Columbus, OH: Merrill Prentice Hall. Kafai, Y., & Bates, M. (1997). Internet Web-searching in the elementary classroom: Building a foundation for information literacy. School Library Media Quarterly, 25(2), 103–111. Kozma, R. B. (1991). Learning with media. Review of Educational Research, 61, 179–211. Kuhlthau, C. C. (1993). Seeking meaning: A process approach to library and information services. Norwood, NJ: Ablex. Kuhlthau, C. C., Maniotes, L. K., & Caspari, A. K. (2007). Guided inquiry: Learning in the 21st century. Westport, CN: Libraries Unlimited. Large, A., & Beheshti, J. (2000). The web as classroom resource: Reactions from users. Journal of the American Society for Information Science, 51(12), 1069–1080. Large, A., Beheshti, J., & Breuleux, A. (1998). Information seeking in a multimedia environment by primary school students. Library & Information Science Research, 20(4), 343–376. Large, A., Beheshti, J., Breuleux, A., & Renaud, A. (1994a). A comparison of information retrieval from print and CD-ROM versions of an encyclopedia by elementary school students. Information Processing & Management, 30(4), 499–513. Large, A., Beheshti, J., Breuleux, A., & Renaud, A. (1994b). Multimedia and comprehension: A cognitive study. Journal of the American Society for Information Science, 45(7), 515–528. Large, A., Beheshti, J., Breuleux, A., & Renaud, A. (1995). Multimedia and comprehension: The relationship between text, animation, and captions. Journal of the American Society for Information Science, 46(5), 340–347. Large, A., Beheshti, J., Breuleux, A., & Renaud, A. (1996). The effect of animation in enhancing descriptive and procedural texts in a multimedia learning environment. Journal of the American Society for Information Science, 47(6), 437–448. Large, A., Beheshti, J., & Rahman, T. (2002). Design criteria for children’s Web portals: The users speak out. Journal of the American Society for Information Science, 53(2), 79–94. Lathrop, A. (1989) Online information as a research tool in secondary school libraries. In A. Lathrop (Ed.), Online and CD-ROM databases in schools: Readings (pp. 287–339). Englewood, CO: Libraries Unlimited. Leckie, G. J., Pettigrew, K. E., & Sylvain, C. (1996). Modeling the information seeking of professionals: A general model derived from research on engineers, health care professionals, and lawyers. The Library Quarterly, 66(2), 161–193. Lee, H. W., Lim, K. Y., & Grabowski, B. L. (2008). Generative learning: Principles and implications for making meaning. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll, M. P. (Eds.), Handbook of research on educational communications and technology (3rd ed.). (pp.111–124). Mahwah, NJ: Lawrence Erlbaum. Liebscher, P. & Marchionini, G. (1988). Browse and analytical search strategies in a full-text CD-ROM encyclopedia. School Library Media Quarterly, 16(4), 223-233. Lim, C. P., & Tay, L. Y. (2003). Information and communication technologies (ICT) in an elementary school: Students’ engagement in higher order thinking. Journal of Educational Multimedia and Hypermedia, 12(4), 425–451. Lohr, L. L., & Gall, J. E. (2008). Representation strategies. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll, M. P. (Eds.), Handbook of research on educational communications and technology (3rd ed.). (pp. 85–96). Mahwah, NJ: Lawrence Erlbaum. Lu, Y-L. (2010). Children’s information seeking in coping with daily life problems: An investigation of fifth- and sixth-grade students. Library & Information Science Research, 32, 77–88. Mancall, J. C. (1984). Training students to search online: Rationale, process, and implications. Drexel Library Quarterly, 20, 64–84. Mancall, J. C., Aaron, S. L, & Walker, S. A. (1986). Educating students to think: The role of the school library media program. School Library Media Quarterly, 15(1), 18–27. Marchionini, G. (1989). Information-seeking strategies of novices using a full-text electronic encyclopedia. Journal of the American Society for Information Science, 40(1), 54–66. Marchionini, G. (1995). Information seeking in electronic environments. Cambridge, MA: Cambridge University Press.

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Chapter 5

I-LEARN: A Model for Learning with Information

Abstract This chapter’s presentation of the I-LEARN model is the heart of the book. It is also the longest chapter—using detailed text and a series of illustrations to explain the author’s model for learning in information-rich environments. A description of information behavior that extends traditional information-seeking models into one focused directly on learning, the model provides a blueprint for developing the concepts and skills required for meaningful learning in the information age. The chapter explains and expands I-LEARN’s grounding in ideas presented in the previous chapters and illustrates its application in both formal and informal information-rich environments. Recursive rather than linear, the model includes six stages and eighteen elements that intertwine and overlap. These stages and elements are presented as concepts rather than as specific steps to underscore the model’s flexibility and applicability in a wide range of settings. Detailed examples provide extensive guidance for conceptualizing and implementing it. The chapter is the culmination of the book’s argument that the world itself is the ultimate information-rich environment and that the ability to access, evaluate, and use all types of its information is the key to twenty-first-century learning. To be efficient and effective learners in the information age, individuals must be skilled managers of all the information-rich environments that surround them. Whether “digital natives” or “digital immigrants,” they must be able to access, evaluate, and use various kinds of information as the basis for learning across the full spectrum of human knowledge. In other words, they must be information literate. As noted in Chap. 4, they must be able to recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information. … Ultimately, information literate people are those who have learned how to learn. … They are people prepared for lifelong learning because they can always find the information needed for any task or decision at hand. (American Library Association, 1989).

This definition of information literacy is significant because it makes explicit the essential link between learning and information use. It expands earlier notions of information literacy—a general ability to access, evaluate, and use information—to D. Neuman, Learning in Information-Rich Environments: I-LEARN and the Construction of Knowledge in the 21st Century, DOI 10.1007/978-1-4419-0579-6_5, © Springer Science+Business Media, LLC 2011

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pinpoint the ultimate reason for information behavior, which is to gather and use information as a basis for learning. While interest in other types of literacy has come to the fore in recent years—technology literacy, digital literacy, multimedia literacy, etc.—the focus of information literacy on information itself rather than on its carriers and containers continues to place information literacy at the center of the learning process. More recent definitions of information literacy echo the “learning” focus of the 1989 American Library Association definition. The Association of College and Research Libraries, for example, noted in 2000 that Information literacy forms the basis for lifelong learning. It is common to all disciplines, to all learning environments, and to all levels of education. It enables learners to master content and extend their investigations, become more self-directed, and assume greater control over their own learning. (p. 2)

Even more recently, high-level meetings co-sponsored by UNESCO, the International Federation of Library Associations, and the National Forum on Information Literacy heightened international interest in information literacy with the Prague Declaration (2003) and the Alexandria Proclamation (2005). These documents not only linked information literacy to learning but also positioned information-based learning as a basic human right: Information literacy lies at the core of lifelong learning. It empowers people in all walks of life to seek, evaluate, use and create information effectively to achieve their personal, social, occupational and educational goals. It is a basic human right in a digital world and promotes social inclusion of all nations. (Alexandria Proclamation, available at http:// archive.ifla.org/III/wsis/BeaconInfsoc.html)

Updated and expanded, the trend continues. In 2007, two associations based in the United States—the American Association of School Librarians and the International Society for Technology in Education—reaffirmed the basic elements of information literacy in their latest learning standards for K-12 students. Two years later, President Barack Obama proclaimed October 2009 as National Information Literacy Awareness Month and called “upon the people of the United States to recognize the important role information plays in our daily lives and [to] appreciate the need for greater understanding of its impact” (http://www.whitehouose. gov/assets/documents/2009literacy_prc_rel.pdf). Current initiatives described in Chap. 4 reinforce this importance and place learning with information at the center of all twenty-first century learning. Across the spectra of age, situation, occupation, and location, then, the concept of information literacy continues to be a critical construct for learning in the information age.

5.1

Information Literacy and Instruction

Despite today’s broad understanding of the importance of information literacy, actual information-literacy instruction remains largely the province of information professionals in college and university libraries and in K-12 school library media centers.

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Further, it consists largely of teaching students to access information, to evaluate it, and to use it to answer particular questions or to complete particular projects or assignments—not to apply these skills beyond the immediate context. National guidelines promulgated by both the K-12 and the postsecondary library and information-science communities flesh out these three stages, detailing multiple standards, benchmarks, and concepts that define the range of knowledge and skills encompassed by information literacy as currently taught. (See Information Power: Building Partnerships for Learning, American Association of School Librarians and Association for Educational Communications and Technology 1998; Infor mation Literacy Competency Standards for Higher Education, Association of College and Research Libraries 2000; and Standards for the 21stCentury Learner, American Association of School Librarians 2007.) Initiatives proposed by groups that extend beyond these communities have opened the way for broader discussion, but this discussion is just beginning. In the context of day-to-day instruction in library settings, the “access-evaluate-use” sequence is too often considered an information-seeking process rather than a learning one. In practice, students are regularly taught how to access various databases and other resources and how to evaluate them for accuracy and relevance to their needs. The actual “use” component is frequently considered outside the realm of library instruction and is left to the student, the teacher, or the professor. The instruction thus precedes and prepares for meaningful learning—and certainly involves some learning along the way—but stops short of focusing on the actual processes and products of learning itself. Despite today’s widespread emphasis on using information as the basis for all kinds of learning, in practice school-based information-literacy instruction—faced with constraints on time, personnel, and resources—often leaves the final, crucial component of information behavior to others. Information-literacy instruction in other venues—for example, the public library—is even less focused on the ultimate use of the information, since library and information professionals are committed to patrons’ right to find and use whatever information they seek without oversight or interference.

5.2

The I-LEARN Model: Introduction

The I-LEARN model—Identify, Locate, Evaluate, Apply, Reflect, kNow—offers a way to make explicit the essential link between information use and learning both within and beyond current instructional practices. By building on the three wellestablished dimensions of information literacy and expanding them to include a specific focus on learning, the model focuses directly on learning with information. The model’s six stages (pictured in Fig. 5.1) and its eighteen elements (to be explained later) illustrate how information is the building block for contemporary learning in all information-rich environments. As Fig. 5.2 shows, the model extends the library-based information-literacy model in several important ways. First, it begins with a key idea in the learning

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Fig. 5.1 The I-LEARN model

process: identifying a particular question or problem that a learner seeks to resolve. Although this stage does not appear in the traditional information-literacy model, research, theory, and practice in both information studies and education support the value of identifying a meaningful problem as the engine for successful information seeking and learning. Gross (1999, 2000; Gross and Saxton 2001), for example, found that information seekers perform more effectively when they do research related to self-generated questions than to imposed ones. Wiggins and McTighe (1998, 2005) promulgated the idea of asking students to identify “essential questions” to guide them in their learning. And Kuhlthau et al. (2007) assume that asking the right question is the basis for what they call “guided inquiry”—a team approach among teachers and library media specialists designed to “develop independent

Fig. 5.2 Information literacy and I-LEARN

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learners who know how to expand their knowledge and expertise through skilled use of a variety of information sources employed both inside and outside of the school” (p. 1). The second way in which the model expands the library-based approach is that it assumes a much wider range of information sources than the recorded materials available on library shelves, in electronic databases, and in other more or less “traditional” information formats. Instead, it sees the world itself as brimming with information inherent in the various information environments in which we live and move. It encompasses, for example, the information about culture, social values, and religious beliefs inherent in the architecture of a medieval cathedral; it includes the information about character, motivation, and language structure inherent in a contemporary film; it considers the information about climate change presented by data on melting glaciers, observed changes in animal behavior, and tales told by older people who remember (however accurately or inaccurately) the deeper snowfalls and colder winters of their youth. All these “materials” are information sources, and all provide the raw material for learning. The third expansion is I-LEARN’s enhanced attention to information use. While the model deals extensively with the information-literacy tasks of access/location and evaluation, it focuses primarily on information use—applying information to the creation of knowledge, reflecting on that creation, and instantiating and personalizing the acquired knowledge. This intensified focus on using information as the central component of learning encompasses not only the familiar reasons for which learners use information—for example, to write papers and create PowerPoint presentations and podcasts—but also on the kinds of information use learners will pursue throughout their lives for such tasks as choosing careers, planning vacations, and making health-care decisions. The model goes beyond the conceptualization of information seeking and use as a clean and linear process that progresses smoothly from the identification of an information need to the full resolution of that need. Although the graphic representations of I-LEARN in Figs. 5.1 and 5.2 are limited by the need to appear fairly simple in order to be useful (as well as by the two-dimensionality of the printed page), they are in fact a top-level representation of a set of concepts that is deep and complex. The model is in fact recursive—its stages overlap, loop back upon one another, and are influenced by many factors throughout the process it represents. While the I-LEARN model has a strong cognitive focus, it follows Kuhlthau (1985, 1993) as well as researchers in affective learning (see Martin and Briggs 1986; Martin and Reigeluth 1999; Small and Arnone 2000) in acknowledging the behavioral and affective components of information seeking and learning as well as the cognitive one. And while it addresses the processes by which individual acts of learning occur, it follows Vygotsky (1978) and the social-learning theorists who have sailed in his wake (see, e.g., Salomon and Perkins 1998) in assuming that learning has social as well as individual aspects. Learning, like any complex process, is messy. In fact, it is arguably the most complex and therefore the messiest process in which humans engage. The I-LEARN model not only accepts that messiness but embraces it. It provides a comprehensive construct for understanding information behavior as well as a practical tool for helping learners (1) to develop

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a habit of mind that looks to the world as a source of never-ending information and (2) to use all types of information to create personally meaningful, actionable knowledge. The model bridges the fields of information studies and instructional development and design by drawing on components of each to create a way to think about learning that responds directly to a world that is itself the ultimate information-rich environment.

5.3

The I-LEARN Model: The Theory

The I-LEARN model is based upon theories and concepts presented in the earlier chapters of this book. It draws upon theories of the nature of information presented both in the information science literature (e.g., Buckland 1991; Dervin et al. 2003; Dervin and Nilan 1986; Marchionini1995; T. D. Wilson 1981, 1999) and in the literature of instructional design (e.g., Gagne 1965, 1985; Hannafin and Hill 2008; Hill and Hannafin 2001; Mayer 1999; Merrill 1983, 1999; Merrill et al. 1992). It assumes that information itself is a dynamic phenomenon represented by entities and relationships that can be mixed and matched according to their natures and the uses to which they are put. It is grounded in the understanding of learning as a sense-making process as conceptualized by Dervin (1992, 1998) and Kuhlthau (1985, 1988, 1993, 1997) and as a constructivist phenomenon as summarized in Bransford et al. (2000). It assumes that learning is an active, dynamic process that consists of creating mental representations that are themselves malleable and evolving. It incorporates contemporary views of twenty-first-century learners as confident yet pressured, worldly wise yet academically naïve, multitasking and technologically astute individuals who live in a world that consists of multiple, overlapping information environments. The I-LEARN model—itself a dynamic construct—encompasses all these dimensions. The model also accommodates constructs related to the information-rich environments described in Chaps. 2 and 3 and to the learning affordances that inhere in the various information objects detailed there—single-sense, multisensory, and standalone and networked interactive. As Chap. 4 indicates, a good deal of research from both information studies and instructional systems design and development is necessary before these ideas can be fully integrated into the theory and practice of learning with information. Nevertheless, I-LEARN is both broad enough in its stages and specific enough in its elements to provide a comprehensive conceptual structure for guiding that research and for incorporating its results into practice. Ultimately, understanding and exploiting the learning affordances of the full range of information objects is a critical component of learning in information-rich environments. At its most basic level, the model incorporates the types of knowledge (“the knowledge dimension”) and the levels of learning (“the cognitive process dimension”) detailed in Anderson and Krathwohl’s (2001) revision of the original “Bloom’s Taxonomy” (Bloom 1956), a framework for designing instruction for over half a century. The revised Taxonomy provides a specific starting place for I-LEARN’s linking of information literacy and learning.

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5.3.1 The Knowledge Dimension As shown in Fig. 5.3, Anderson and Krathwohl (2001) identified four types of knowledge: factual, conceptual, procedural, and metacognitive: • Factual knowledge—The basic elements students must know to be acquainted with a discipline or solve problems in it • Conceptual knowledge—The interrelationships among the basic elements within a larger structure that enable them to function together • Procedural knowledge—How to do something, methods of inquiry, and criteria for using skills, algorithms, techniques, and methods • Metacognitive knowledge—Knowledge of cognition in general as well as awareness and knowledge of one’s own cognition. (p. 46) The authors further divided each type into subtypes: • Factual knowledge—Knowledge of terminology; knowledge of specific details and elements • Conceptual knowledge—knowledge of classifications and categories; knowledge of principles and generalizations; knowledge of theories, models, and structures • Procedural knowledge—Knowledge of subject-specific skills and algorithms, knowledge of subject-specific techniques and methods; knowledge of criteria for determining when to use appropriate procedures • Metacognitive knowledge—Strategic knowledge; knowledge about cognitive tasks, including appropriate contextual and conditional knowledge; selfknowledge. (p. 46)

Fig. 5.3 The knowledge dimension

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Fig. 5.4 maps the types and subtypes of the knowledge dimension to the information-literacy model and suggests a series of relationships among the steps of the information-seeking-and-use process and the type(s) of learning most relevant to each. A brief example illustrates this point. A learner assigned to write a paper on the solar system begins by going to an encyclopedia or a database to access facts and concepts—the definition of “planet,” for example, or a chart that shows the relative distances of the planets and the sun. Then he relies on “appropriate contextual” metacognitive knowledge to evaluate that factual and conceptual knowledge: for example, does the presence of Pluto on the chart suggest anything about its accuracy? Finally, he uses both strategic metacognitive knowledge and a variety of appropriate graphics procedures to create and communicate knowledge from the evaluated information—designing a new chart that is both currently accurate and aesthetically pleasing.

Fig. 5.4 Information literacy and the knowledge dimension


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Of course, none of these first-level relationships encompasses the full meaning of any of the types and subtypes of knowledge: one can access concepts without understanding the “theories, models, and structures” that underlie them, and “knowledge about cognitive tasks” involves far more than understanding how to evaluate an outof-date graphic. Furthermore, there is considerable overlap across the steps of information literacy and the spectrum of cognitive levels: procedural knowledge is involved in accessing information; factual knowledge and conceptual knowledge are subsumed in the creation of new knowledge; and metacognition undergirds the entire process of seeking, evaluating, and using information. The relationships are thus suggestive rather than determinate—but, even so, they reinforce the linkage between information literacy and learning that is at the heart of the I-LEARN model.

5.3.2 The Cognitive Process Dimension This linkage can also be seen through an examination of the relationship between information literacy and Anderson and Krathwohl’s (2001) “cognitive process dimension.” Displayed in Fig. 5.5, this dimension encompasses the six “levels of learning” that appeared in Bloom et al.’s original Taxonomy, although they are treated somewhat differently in the new version: • Remember—Retrieve relevant knowledge from long-term memory • Understand—Construct meaning from instructional messages, including oral, written, and graphic communication • Apply—Carry out or use a procedure in a given situation • Analyze—Break material into its constituent parts and determine how the parts relate to one another and to the overall structure or purpose • Evaluate—Make judgments based on criteria and standards • Create—Put elements together to form a coherent or functional whole; reorganize elements into a new pattern or structure. (pp. 67–68) According to the authors’ research, these six processes describe the full range of human cognitive activity, from the simple recall of distinct facts to the complex use of critical-thinking and problem-solving skills in formal and informal learning situations. As with “the knowledge dimension,” Anderson and Krathwohl (2001) have subdivided these general categories into subcategories that describe the details of each level of learning. While each subcategory is accompanied by a full and lengthy definition in their text, only the key terms and concepts are presented here: • Remember—Recognizing/identifying; recalling/retrieving • Understand—Interpreting/paraphrasing; exemplifying/illustrating; classifying/ categorizing; summarizing/abstracting; inferring/predicting; comparing/mapping; explaining/modeling • Apply—Executing/carrying out; implementing/using • Analyze—Differentiating/selecting; organizing/outlining; attributing/determining point of view

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Fig. 5.5 The cognitive process dimension



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• Evaluate—Checking/testing; critiquing/judging • Create—Generating/hypothesizing; planning/designing; producing/constructing. (pp. 67–68) Fig. 5.6 maps these internal processes and subprocesses to the external steps of the information-literacy model to suggest a series of relationships between information literacy and learning. Once again, our “planet” example can serve to illustrate

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Fig. 5.6 Information literacy and the cognitive process dimension

this point. A learner remembers/recalls facts and concepts stored in long-term memory—a memorized definition of “planet,” for example—and understands/ interprets a mental representation of a planetary chart. Then she sees a television special on the “new” planetary system and analyzes/differentiates his or her existing knowledge in light of the conflicting information presented in the program. She evaluates/judges what is accurate and what is inaccurate and creates/hypothesizes an alternative mental representation to replace what has become incorrect. Throughout the process, she applies/implements appropriate procedures to accomplish each cognitive task. The processes and subprocesses of the cognitive dimension are internal, and their relationship to the processes and subprocesses of information seeking and use is only suggestive. Nevertheless, these processes parallel the stages of information literacy and reinforce the linkage between information literacy and learning that is the foundation of I-LEARN.

5.3.3 Types of Knowledge, Cognitive Processes, and Information Literacy Taken together, the types of knowledge in Anderson and Krathwohl’s (2001) “knowledge dimension” and the processes in their “cognitive process dimension” provide a structure for thinking about the complex relationship of information use and learning. While the relationships among these many complex ideas are overlapping and imperfect, Fig. 5.7 shows that they nevertheless provide a way to begin to think in a systematic way about the role of information as a tool for learning.

Fig. 5.7 I-LEARN and Anderson and Krathwohl’s (2001) Taxonomy

5.4 The I-LEARN Model

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In the accepted model of information literacy—access, evaluate, and use— learners and others generally access the types of knowledge labeled factual and conceptual. The levels of learning associated with factual and conceptual knowledge are, respectively, remember and understand. The next step of information seeking—evaluating—involves using the type of knowledge labeled metacognitive, and the levels of learning most closely associated with metacognition are analyze and evaluate. The final step of the information-literacy model—use—involves using all the types of knowledge, including those known as procedural and metacognitive; the levels of learning most closely related to this step are apply and create. In the process of learning with information that is reflected in the information-literacy model, learners use factual, conceptual, metacognitive, and procedural knowledge as the basis for generating new understandings. They remember or identify facts and concepts, analyze and evaluate the information content of those facts and concepts, and apply a variety of strategies and techniques to create and communicate the new knowledge they gain in the process.

5.4

The I-LEARN Model

Drawing on the theoretical constructs noted above, the I-LEARN model links information behavior with learning behavior through the series of elements related to each of its six stages, as shown in Fig. 5.8:

Fig. 5.8 I-LEARN stages and elements

It is not coincidental that the “I” in the initial stage suggests several concepts in addition to “Identify”: the dependence on Information as the building block for learning is clearly implied, as is the personal responsibility for one’s own learning assumed by constructivist learning theory (“I create my own understanding of the world”). Further, it is important to note that the model’s “kNow” stage ends with the element entitled “activate”—the same element that begins the learning process under “Identify.” The implication is that greater knowledge about the informationrich nature of the world around us is likely to stimulate even more curiosity about its essence, structures, and processes. The following sections of this chapter illustrate how the stages and elements might play out in practice. While there are related ideas that the sections do not address—for example, the complexity and recursive nature of the overall process,

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the role of specific learning affordances, the ethical dimensions of information seeking and use, and the persistence (or lack thereof) required at each I-LEARN stage—the sections offer extensive examples to suggest the model’s practical application.

5.4.1 Stage 1: Identify The first stage in the model, as shown in Fig. 5.9, refers to choosing a topic to investigate, a problem to solve, or a question to answer. The focus can be large or small—the evolution of Chinese culture or the reason water boils. It can be academic or personal—the Westward expansion or the range of hotels in a vacation destination. It can be immediate or long-term—the requirements for a driver’s license or the choices among retirement plans. All that’s necessary is that the underlying issue can be addressed with information (rather than emotion). While a larger topic, problem, or question will require more extensive and complex information—and more extensive and complex learning—than a smaller one, any area that can be addressed with information can be “identified.”

Fig. 5.9 Stage 1: Identify

To “identify” an area, a learner must activate a sense of curiosity about the world in which he or she lives, scan that world (either physically or virtually) for a suitable subject, and formulate a targeted problem or question that will guide his or her inquiry. This stage is the touchstone for independent learning, since identifying a problem not only initiates the learning process but arises from a habit of mind that sees the world as a source of information that can support the learning of virtually anything of interest to an individual or a group. This habit of mind recognizes the world as a source of never-ending information that can be used to solve problems and improve lives—that is, as the ultimate information-rich environment. Developing and maintaining such a habit of mind is the beginning of lifelong learning. Scanning the world to select a subject for inquiry is a natural outgrowth of cultivating a sense of curiosity. In formal learning environments, such subjects are


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often presented to students; even here, however, research has shown that allowing students to select their own research topics yields higher motivation and increased learning (Gross 1999, 2000; Gross and Saxton, 2001). Scanning obviously occurs in informal environments as well, and learning to scan in a way that suggests questions is a key part of the I-LEARN model. Looking at a cathedral and seeing only a pile of stones and spires does not lead to an opportunity for learning. Looking at that same cathedral with curiosity about the society that built it, the skills of the artisans who carved and glazed it, and the rites and rituals performed within it, however, offers myriad opportunities for intellectual and personal growth. Turning observations into specific questions that can be answered with information is the final piece of I-LEARN’s “identify” stage. Library and information specialists working with “formal” information seekers call this step “formulat[ing] questions based on information needs” (American Association of School Librarians and Association for Educational Communications and Technology 1998, p. 10) and have created extensive research-based guidelines on how this formulation can be done efficiently and effectively (see, for example, Kuhlthau 1993; Pettigrew et al. 2001; Jamie McKenzie’s Questioning Toolkit at http://questioning.org/Q7/toolkit. html and the many links from that site). While I-LEARN acknowledges the usefulness of such strategies in libraries and other formal information settings, part of the model’s utility is its extension of “question formulation” based on “information needs” into informal information environments. For example, a child might ask what causes a rainbow that arcs over the back yard or a tourist in Italy might ask why the labels on some chiantis (but not all) bear the sign of the black rooster. The key is not the nature of the information environment, formal or informal, but the formulation of a targeted question that can be answered with information. Identifying an appropriate question—that is, one that can be answered with information—is the foundation of using information as a tool for learning. With practice, learners can distinguish such questions from others (“Should I marry him/ her?”) and even see the information aspect of questions that are primarily emotional (“Does he/she have job skills that will allow us to live as I wish to live?”). Cultivating a habit of mind that sees the world as the ultimate information-rich environment, seeing that world with an active sense of curiosity, scanning it for an interesting topic to investigate, and formulating an information-based question or questions to guide that investigation are all parts of this first, critical stage in I-LEARN.

5.4.2 Stage 2: Locate The “locate” stage displayed in Fig. 5.10 refers to accessing information, either recorded or in the broader information environment, that will provide the building blocks for learning. In order to locate information for learning, a learner must focus on what is to be learned, find information that is related to that learning, and extract the most relevant and salient aspects of that information for the learning task at hand.

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Fig. 5.10 Stage 2: Locate

In a library or classroom setting, this stage would generally begin with the kinds of activities outlined in the standards and benchmarks of the K-12 and postsecondary guidelines noted above: “determin[ing] the nature and extent of the information needed” and “identif[ying] a variety of types and formats of potential sources for information” (Association of College and Research Libraries 2000, p. 8) and “develop[ing] and us[ing] successful strategies for locating information” (American Association of School Librarians and Association for Educational Communications and Technology 1998, p. 10). These activities would be carried out in the context of recorded information—books, databases, websites, etc.—available in the setting. In most cases, information would be sought to solve a particular learning problem or complete a specific assignment—for example, preparing a multimedia report on the rainforest. In terms of the I-LEARN model, these activities are all a part of locating information. For example, a learner working on a report on the rainforest might focus on a particular part of the rainforest—say, the birds to be found there—determining what facts and concepts are needed and formulating a search to address that defined information need. He would find sources likely to contain information that would address the topic—say, the “R” encyclopedia volume and the National Geographic website—and then find the particular parts of those sources most likely to contain the information. (Our learner would not look for information about rainforest plants, for example, since this information would not be relevant to the report at hand.) Finally, the learner would extract specific information from the sources that would form the basis of the report—text, visuals, and even audio clips of bird songs—that would convey the depth of his understanding of rainforest birds. While the I-LEARN model encompasses this kind of “locating” in typical information settings, it also describes the process of locating information in a wider variety of information sources. For example, a learner in a course on Shakespeare must also locate the information that is appropriate to her defined need. If she is studying the themes in Hamlet, the appropriate focus is on the text—the passages that reveal Hamlet’s indecision, Ophelia’s despair, and Polonius’ treachery. That same learner, now studying the stagecraft of Hamlet as executed by the college drama society, must focus on the production: the actors’ movements and costumes, the lighting, and the set. Although thematic aspects of the play are certainly revealed in the production and production values are suggested in the text, an


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efficient and effective learner will focus on the venue that contains the richest information for the particular learning task. In that sense, what is to be learned determines the appropriate focus. Next, our learner must find candidate information that will help him or her accomplish the learning task: for example, specific scenes and passages that reveal Hamlet’s vacillation or Polonius’ conniving or specific costuming choices that suggest the period in which the production was set. This process involves selecting a variety of written statements (in the case of the text) or visual images (in the case of the production) so that she can consider the merits of each rather than rushing to the first or most obvious instance to resolve the issue. Finally, our learner must extract the most important information related to her learning goal. Not every candidate passage illustrates Hamlet’s indecision with equal force, and not every candidate photo presents an equally compelling depiction of the director’s choice to set the production in, say, the1950s. Ultimately, the learner might well choose Hamlet’s famous “To be or not to be” soliloquy as a key marker of his indecision or the photo of the travelling players with hula hoops as the best evidence for a quirky timeframe for the production. A student of science (or, for that matter, a practicing scientist) would follow a similar trajectory to locate information: for example, focusing on a specific question about the chemical composition of plastic bottles; finding candidate information related to that question in scientific journals, conference proceedings, government and industry reports, consumer blogs, etc.; and extracting the most relevant and salient information to guide his own experiments to determine whether and to what extent a particular chemical affects human health. In all three of these examples—one related to “library research,” one related to the study of literature, and one related to scientific experimentation—the learner’s key activity is to locate the information necessary to solve the learning problem. Whether that information is explicit (available in recorded formats designed to collect, organize, and present information) or implicit (embedded in the informationrich environment of the world around us) a learner must focus on what is to be learned, find the information that will meet that learning need, and extract the information that is most germane to that goal.

5.4.3 Stage 3: Evaluate If the “locate” stage of the I-LEARN model is based on activities related to the traditional information-literacy model, the “evaluate” stage is even more closely tied to that approach. In this stage, the learner “evaluates information and its sources critically” (Association of College and Research Libraries 2000, p. 11) by “determin[ing] accuracy, relevance, and comprehensiveness … distinguish[ing] among fact, point of view, and opinion … and identify[ing] inaccurate and misleading information” (American Association of School Librarians and Association for Educational Communications and Technology 1998, pp. 14 –15). Similarly, in the

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I-LEARN model the “evaluate” stage refers to judging the quality of the information itself. While there are many aspects of information that might be evaluated, Fig. 5.11 displays three that seem especially important in evaluating information as a building block for learning: authority—the credibility of the source and the author and the accuracy, completeness, and internal logic of the information; relevance— the applicability of the information to the topic at hand and its appropriateness in terms of the nature of its content related to the developmental level of the learner and the level of learning required; and timeliness—its currency and its physical accessibility for a particular learner and task. Fig. 5.11 Stage 3: Evaluate

Unlike the other stages in the model, each of which assumes a sequence of tasks within itself, the “evaluate” stage does not suggest that one evaluative task follows another. For example, both identifying a researchable question and locating information to answer it involve a continual narrowing of focus toward a specific outcome. The process of evaluating information, however, is not sequential: one does not consider authority before relevance or relevance before timeliness. In practice, judging these aspects of information is an overlapping, iterative process. The important thing in this stage is not the order but the totality: all three aspects are generally judged simultaneously, and none of them should be overlooked. Evaluating the quality of recorded information is at the heart of library research, and the concepts and processes related to this stage have been the focus of decades of research in information studies (see, for example, P. Wilson 1968; Rieh 2002, 2010). The emergence of electronic information sources, and particularly the rich information environment of the Internet/Web, has made the task exceptionally complex in recent years. Most of us now go to the Web in our initial attempts at finding information, and countless suggestions related to criteria for judging the quality of websites have been published in the scholarly and popular literature (see, for example, Harris 2005). Even in the relatively “safe” world of respected sources, learners must understand how to evaluate information skeptically. For example, school library media specialists use “doctored” Civil War photos in the Library of Congress’s “American Memory” collection to encourage students to view even the most highly regarded sources with skepticism. One photo shows the “bodies” of Confederate soldiers killed in battle, while another shows the same “bodies” in different poses and wearing Union uniforms. With both respected and questionable sources, then, evaluation can never be overlooked.


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The I-LEARN model incorporates the criteria developed by scholars and practitioners in information studies and relies on the wealth of guidance in that field as the basis for evaluating all information, recorded and implicit. Learners can draw on many of these criteria to evaluate information’s authority—not only the credibility of a Wikipedia article but also the credibility of televised ads created by marketers whose goal is to sell products and the statements of radio commentators of all stripes—conservative, liberal, and in-between. They can consider these criteria as they determine the accuracy of statistics tracking trends in population growth and in consumer spending. They can use them to judge the completeness of the information included in a warranty or a sales contract or the internal logic of a social-network site devoted to saving the whales or to restricting stem-cell research. In short, the I-LEARN model suggests that learners should develop a habit of mind that predisposes them to assess the authority of the information inherent in every information-rich environment they encounter. Learners who understand the role of learning affordances in the various information objects they encounter—for example, the purpose of a jump cut or a specific camera angle in a retrieved video—can draw on this understanding as part of their evaluation of authority as well. While there is currently little research or practice to guide this facet of evaluation, it is clear that knowledge of such aspects of information representation as the “filmic code” (Salomon 1974) could have a marked effect on students’ ability to evaluate the quality and authority of information. Learners can also draw upon ideas developed through decades of informationstudies research to make judgments about the relevance of information (see, for example, Cuadra and Katter 1967; Schamber et al. 1990; Barry 1994; Schamber 1994; Hirsch 1999; Raber 2003; Saracevic 1975, 2007a, b; White 2010a, b). A complex and widely debated concept, relevance is perhaps best described as “a multidimensional cognitive concept whose meaning is largely dependent on [learners’] perceptions of information and their own information need[s] … a dynamic concept that depends on [learners’] judgments of the quality of the relationship between information and information needs at a certain point in time” (Schamber et al. 1990, p. 774). This description clearly echoes the dynamism of information and of learning itself, while it also suggests the complexity involved in judging relevance. Clearly, information must be topically relevant—that is, directly applicable to the topic at hand—to serve as a building block for learning. While this idea is selfevident in theory, judging the relevance of particular pieces of information is often very challenging, especially for learners—who are, by definition, novices in the topic they’re trying to learn and whose judgments about relevance can be skewed (Neuman 1995, 2001). How would a novice know, for example, that an article about fresh-water habitats has little to offer to a project on marine biology (Pitts 1994)? Or that a DVD about Picasso is less relevant to the study of Impressionism than one on Monet? Or that statistics related to the immigration and settlement of Europeans in the United States after World War I could be used to shed light on the experiences of the Hmong after the war in Vietnam? Evaluating topical relevance is an especially difficult and important task for learners. I-LEARN recognizes this complexity and therefore highlights it as a key element of evaluation for learning.

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Appropriateness is another issue related specifically to the need to evaluate information as a tool for learning. To be appropriate for learning, information must exist at the “right” level of complexity for both the learner and the learning task. Information in the Chemical Abstracts database, for example, might be both authentic and related to the topic at hand but inappropriate for high school science students because of its depth and sophistication and its limited applicability to the learning tasks such students are likely to encounter (Neuman 1995). Basic flow charts might be appropriate to help beginners understand how to design and create computer programs but far too simple to serve even as reference documents for experienced programmers. Evaluating appropriateness, like evaluating relevance, is multidimensional and highly individual. Learners need to understand how to choose information that will enable them to achieve their learning goals—and should be encouraged to become increasingly sophisticated in their choices as they advance in their own knowledge. Finally, information must be timely if it is to meet learning needs. One aspect of timeliness is currency—but only as that construct relates to particular learning tasks. Obviously, a video clip on the “planet” Pluto will not help a twenty-firstcentury learner understand the solar system; a collection of Nazi propaganda photos from the 1940s, however, would be perfectly “timely” for a study of World War II. Understanding this kind of timeliness is especially important for young learners, who tend to believe the information-rich environment of the Internet / Web provides the “best” information for resolving all questions, even those best answered by the world of sources that have not been digitized. Pulling information from a website created “yesterday” without considering what other sources might offer—a common strategy for students and others—can lead to misunderstandings related to lack of this kind of timeliness. Another aspect of timeliness is related to accessibility, or the ability of learners to acquire good information quickly and easily enough to meet deadlines imposed by teachers, personal schedules, or professional obligations. Getting one’s hands on authoritative and relevant information, whether to finish a school assignment on time or to purchase a car during the end-of-model-year sales, is a critical part of using information to learn. Information that cannot be accessed in time to solve an information problem or answer an information question is simply not useful as a learning tool. As it has done in so many areas, the information-rich Internet / Web environment has added a new dimension to this aspect of timeliness. Years ago, learners were restricted to searching for information that was often available only in environments accessible by foot, car, bicycle, or public transportation. It took time to get to information repositories, let alone to find good information. Today, learners can find information instantaneously through devices that we carry in our pockets and purses. Then, information seeking involved painstakingly finding a few “good” resources among the limited (but vetted) possibilities that were physically accessible. Now, it involves skimming through the limitless (often unvetted) information on the Internet / Web that is available at lightning speed—and discarding resources rather than gathering them. Given many students’ predisposition to procrastinate—as well


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as to finish assignments in the least amount of time possible—the dangers of this kind of information seeking are clear. Perhaps more importantly in terms of learning, the two ways of information seeking are almost completely different in the type and level of cognition required: gathering is largely a “synthetic” task, while discarding is largely an “analytic” one. Within I-LEARN, the challenge is to combine the two approaches as part of the overall evaluation process. The goal is to select information from a variety of sources because that information is authoritative, relevant, appropriately current, and reasonably available; the danger is to overlook such information when it is more than a mouseclick away. Evaluating information for learning is a complex and demanding task. While there are many ways to approach it—and many books and instructional materials that describe the process and its pitfalls—I-LEARN suggests that focusing on the authority, relevance, and timeliness of information is especially important for learning. Other aspects of information can certainly be evaluated in this stage, but these three are obvious starting points.

5.4.4 Stage 4: Apply Fig. 5.12 shows the stage of I-LEARN that is at the heart of the model: it represents the major expansion of the information-seeking paradigm into a model for learning and it directly addresses the concepts and mechanisms involved in using information as the fundamental building block for learning. Although it is clearly related to concepts discussed by Dervin (1992, 1998, 2003) and Kuhlthau (1985, 1993, 1997), it begins to move I-LEARN into territory more familiar to instructional designers and teachers than to many information professionals.

Fig. 5.12 Stage 4: Apply

The “apply” stage focuses on how a learner uses information to generate new, personalized knowledge from information; to organize that information-based understanding into some kind of mental representation; and, in school settings and elsewhere, to create representations that communicate that new understanding

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in a usable way. These three aspects are drawn directly from contemporary learning theory, which conceptualizes learning as an active, dynamic, personal, and selfdirected process by which we make sense of the world around us (see Chap. 1). As Bransford et al. (2000) noted, “In the most general sense, the contemporary view of learning is that people construct new knowledge and understandings based on what they already know and believe” (p. 10). I-LEARN is grounded in this constructivist view of learning, which assumes that learners relate pieces of information to one another in both general and specific ways in order to generate the personal mental representations we call “learning.” In many cases, the generation of these relationships is straightforward, apparently effortless, and virtually invisible. What I-LEARN adds is the encouragement to focus specifically on the process of generating both simple and complex relationships rather than relying on intuition, instinct, or another ethereal method to guide the constructive process. Behind Bransford et al.’s (2000) deceptively simple statement are decades of research on the nature and processes of learning—not only on the types and levels of learning noted by Anderson and Krathwohl (2001) that underpin I-LEARN but on various kinds of learning (e.g., rote and meaningful), mechanisms of learning (e.g., chunking and dual coding); stages of learning (e.g., short-term and long-term memory); styles of learning (e.g., verbal and auditory); structures of learning (e.g., schemas and mental models); and more. In addition to studies emanating from educators and cognitive psychologists, ideas from the fields of the “learning sciences,” including cognitive neuroscience and neurobiology, have added to our understanding of the biological and physiological workings of the brain as it learns. Integrating the thousands of studies that have informed our current understanding of learning is, of course, well beyond the scope of this book. The I-LEARN model is intentionally broad enough to accommodate ideas from all currently accepted learning theories. The focus of the “apply” stage of the model is the ways that learners take the information they have located and evaluated to answer the question or solve the problem they had identified to start the learning process. While explaining the specifics of this cognitive process is best left to cognitive psychologists and similar professionals, it is clear that learners generate new understandings by relating pieces of information to one another in a variety of ways. For example, a young child relates individual letters to one another in a particular order in order to learn the alphabet; a high school or college student relates kinds of data to the specific tests that can be used to analyze those data in order to learn statistics; a senior citizen relates information about particular health needs to the offerings of various potential vendors in order to learn which Medigap insurance plan to choose. An important component of this stage is its emphasis on the personal aspect of forming these relationships: each individual generates his or her own personal understanding of the topic at hand. That understanding does not have to be “new” to the world at large—it doesn’t need to overturn Einstein’s theory of relativity— but it has to be new to the learner and understood by him or her as his or her own construction. Even learning the alphabet is, after all, new learning for a child and a highly personal task whose accomplishment she generally communicates with


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great pride. Furthermore, whether a learner is working alone or as a member of a group, whether he is supported by a pencil and notepad or by the latest technology, in the end the act of generating meaning is the individual’s alone. While I-LEARN encompasses both socially assisted learning and technology-assisted learning, it sees the construction of any new mental representation as ultimately a highly personal and individual event. In some subject areas, the relationships of pieces of information are obvious. For example, the genealogical relationships of the kings and queens of England are clear—and they are learned by all English schoolchildren. The relationships of organisms in the Linnaean taxonomy, once learned by virtually all biologists, are still used as a starting point when these scientists classify newly discovered species. The relationship of tobacco to lung cancer, although not understood in detail by nonmedical personnel, has been learned so fully by the public at large that it is a primary impetus for smokers to quit. In other subject areas, especially those that are grounded in abstractions rather than in physical realities, the “pieces” of information are often larger and more complex. And their relationships are often far less clear. What is the relationship between an increase in the price of oil and a decrease in consumer spending? Between Tchaikovsky’s 1812 Overture and Napoleon’s invasion of Russia? Between a Supreme Court ruling on affirmative action and a change in enrollment patterns in colleges and universities? These relationships are subtle, complex, and multidimensional—but generating a personal understanding of them is what happens when learning occurs. Like Jonassen et al. (1993), I-LEARN suggests that focusing specifically on how pieces of information relate to one another is critical to using information as a tool for learning. There are, of course, many ways in which pieces of information might be related to one another: logically, temporally, causally, semantically, hierarchically, correlationally, and more—even arbitrarily. Learning the history of a country, for example, involves building an understanding of how events are related temporally, politically, economically, sociologically, geographically, etc. Examples of types of relationships are endless. I-LEARN suggests that attending directly to the ways in which pieces of information are related—and, equally importantly, to the instances in which relationships do not exist—is critical to learning with information. Government data on the relationship of obesity to diabetes are likely to express valid causal relationships, for example, while celebrities’ endorsements of particular weight-loss programs are far less logically compelling. Statistics professors who drill their students in the mantra that “correlation is not causation” offer a prime example of the importance of knowing when relationships do and do not exist. The problem of understanding how pieces of information relate (or don’t) is especially challenging for twenty-first-century learners, especially those working in informal information-rich environments like museums, public libraries, and— increasingly—the home. In such environments, learners are often without “traditional” information vehicles that provide both explicit and implicit content structures to guide them in the generation of new understandings of how ideas

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relate: a trip to an art museum, for example, is a wonderful opportunity for learning from the information inherent in the paintings and sculptures on display, but that learning is rarely guided by formal learning materials that detail the relationship of Greek mythology to Greek sculpture. In more formal information-rich environments—like schools—instructional materials, textbooks, and even reference books draw upon a number of conventions that suggest the works’ structures and offer ways students can understand how the information in the works is related. An index or table of contents, for example, or chapter headings and subheadings and questions at the end of a chapter provide indicators of the organization that the authors have imposed on the overall content of a printed work. Graphic design and layout—font sizes and styles for different levels of heading, for example, and color and white space for setting off different kinds of information—reinforce this organization and help learners develop a sense of the relationships among ideas in the work, almost by osmosis. Think, for example, how levels of headings—the sizes and styles of the chapter titles, main headings, and subheadings—provide visual information about the structural relationships across various segments of a work. The information-rich environment of the Internet / Web offers special challenges to learners trying to relate pieces of information. Although individual websites are often clearly organized and use many print-based conventions to underscore that organization, many sites do not make their internal structures apparent. And, of course, in practice learners move from website to website, taking one idea from here and another from there without necessarily seeing the organization of the individual sites, let alone an overarching structure. Learning in such an environment requires that learners themselves have the built-in skills and understanding necessary to generate their own sensible mental representations from the discrete pieces of information they encounter. And although learners have been dipping into and out of printed works for centuries—and not always coming away with reasonable cognitive structures—the vast quantity of material on the Internat/Web and the speed and ease with which learners can move within and across a range of sites have made the issue especially critical. I-LEARN suggests that the prevalence of the Internet/Web as an informationrich environment often used for learning argues for specific attention to the relationships among and across the disparate pieces of information found there—first to understanding them and then to forging sensible connections among them. While high-ability learners might be able to understand how ideas gathered through Web searching connect, others are not. Neuman (2001, 2003), for example, found that middle-school students needed considerable guidance to see that the “animal facts” they found through searching several databases were not simply isolated ideas but that they could be related to one another in a cohesive and functional way. The next element of “apply”—organize—is really an expansion of generate rather than a separate component. “Generate new personal understandings” is simply another way of saying “create mental models”—and mental models are by definition organized and coherent (yet subjective) cognitive structures. I-LEARN separates the overarching concept of building mental representations into two parts


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only to emphasize the importance of focusing systematically on the creation of such representations—understanding their interrelationships and taking care to generate cognitive structures of them that are coherent and reasonable. Generate and organize are two sides of the same coin, and they often occur simultaneously and iteratively rather than in sequence. The third element of “apply”—communicate—is generally more important in formal learning environments than in informal ones: a student in an art history course might have to create a report on that trip to a museum, while a tourist or other casual visitor would not have to meet such a requirement. (One suspects that museums would be almost empty if formal reports were required!) In formal environments, evidence that learners are generating understandings is available only through the products they create to communicate those understandings, and there is strong emphasis on creating such products. Even in informal environments, however, learners often create products that communicate their understandings: a list of the qualities wanted in a new car, a business plan to undergird an application for a loan to launch a new company, an itinerary to guide a vacation in New Zealand. In both kinds of information-rich environments, formal and informal, the products used to communicate understanding should be consistent both with the nature of the information and with the learning task at hand: a timeline to communicate dates when historical events happened or project milestones are due, a drawing or photograph to communicate the underlying structure of a building or the effectiveness of its decorative elements, a video to communicate the process of conducting a physics experiment or cooking a complicated dish. I-LEARN suggests that the key to communicating is to consider the variety of communicative structures available and to choose an appropriate one among them—rather than rushing directly to the written or oral report. While such familiar formats can certainly communicate understanding, learning with information includes a specific focus on creating information products that use the most appropriate formats to communicate their ideas. Such products are, of course, information objects in their own right. Moreover, knowing how to marshal the learning affordances of various kinds of information objects is likely to have a strong impact on the quality of learners’ creation of them in both formal and informal settings. Would a single-sense object like a series of charts or an interactive one like a computer simulation be more effective for a highschool student’s end-of-term project on the changing economy of the auto industry? For a quarterly report on the same topic that the vice president of an auto company must deliver to the board of directors? Why or why not—in each case? What specific affordances of each type of object make one approach better than the other? How might these affordances be incorporated most effectively in whichever type of information object is created? The answers to these questions are complex, and little is yet known to guide us in their answers. As noted in Chap. 4, understanding learning affordances and how to capitalize on them could have a profound impact on the quality and effectiveness of the information objects that students and others create to communicate their knowledge.

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In summary, the “apply” stage of I-LEARN is the central component of the model because it deals specifically with the key constructs and processes involved in using information as a tool for learning. Although psychologists and others studying the brain and learning do not yet understand specifically how learners use information to generate knowledge from information and organize that information-based understanding into cognitive structures, educators and instructional designers have long experience in guiding learners to create products that communicate their understanding effectively. I-LEARN builds on our current knowledge of learning theory and is broad enough to encompass new ideas about learning that are sure to emerge from ongoing research.

5.4.5 Stage 5: Reflect The idea of reflection as a part of learning is not new: John Dewey discussed it as an ongoing part of the learning process in How We Think (1910), and Donald Schon re-popularized the idea when he differentiated between reflection in action (i.e., thinking on our feet) and reflection on action (i.e., considering why and how we did what we did) in his highly influential book The Reflective Practitioner (1983). Currently, the importance of reflection as an ongoing process that occurs throughout the learning experience is gaining renewed attention. I-LEARN encompasses this concept and considers reflection to be the continual application of critical thinking to assess both the processes and the products of learning throughout the learning experience. The formal introduction of “reflect” as stage 5, as shown in Fig. 5.13, represents its logical placement in a sequential presentation of the I-LEARN model. However, it is important to note that its elements of analyze, revise, and refine are recursive and iterative throughout the process of learning with information. Fig. 5.13 Stage 5: Reflect

Information Power: Building Partnerships for Learning suggests that a reflective learner is one who “actively and independently reflects on and critiques personal thought processes and individually creates information products … recognizes when these efforts are successful and unsuccessful, and develops strategies for revising and improving them in light of changing information”


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(American Association of School Librarians and Association for Educational Communications and Technology 1998, p. 29). Indeed, the idea of reflection is especially important to library media specialists and other school-based information professionals: analyzing, revising, and refining are common components of doing library research and creating products based on that research. A “process” discipline rather than a “content” one, information literacy places a premium on the elements included in I-LEARN as “reflection.” To analyze the adequacy of his or her processes and products related to learning with information, the learner would reflect on his or her performance throughout the learning experience and then examine that performance in a more systematic way toward the end of the process. To do this, he or she would retrace the steps taken to this point and assess each step as it relates to the problem identified at the beginning of the I-LEARN process. A learner in a formal information-rich environment—for example, a middle-school girl working on a project for a science fair—would want to be sure she has located a wide range of information on the topic, evaluated it for such aspects as currency and appropriateness for her level of understanding, applied the information to design and conduct an innovative experiment, and analyzed the quality of the form and content of the product used to communicate the results of her learning—for example, in a poster showing the steps and outcomes of the experiment. A learner in an informal information-rich environment—for example, a conscientious “soccer dad” addressing the problem of how to vote in an election— would want to be sure he has located and looked at a variety of news stories as well as official campaign literature, evaluated the quality and relevance of all the information gathered, applied the information effectively by generating/organizing his own understanding of it into a tentative structure, and analyzed the coherence of the adequacy of that structure and the clarity and utility of a representation related to it—for example, a mental or physical list of the advantages and disadvantages of each candidate. In either environment, formal or informal, the details of the stages could vary widely. The overall idea, however—analyzing the adequacy of one’s work, both in its process and in its outcome—remains the same. The next element of “reflect” is revise—making whatever adjustments are necessary to improve both the process and product according to the shortcomings the analyze element reveals. Was the information that formed the basis for learning superficial or incomplete? Perhaps our science-fair student should talk to a working scientist to expand the quality of her information gathering; perhaps our prospective voter should consult some mainstream and “edgy” blogs to uncover additional ideas. Was the product that communicated the learning less clear and compelling than it should be? Perhaps our student could use color and graphics to highlight key points; perhaps our voter could recast his random list of pros and cons into one organized according to particular issues. Again, each element of revise suggests an endless variety of possible ways to approach the task. Rather than detailing all these possibilities, I-LEARN focuses on the general principles of remaining alert to the value of revision and continually seeking ways to improve the generation and communication of learning.

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The third element of this stage—refine—refers to polishing the results of the overall learning process. This polishing might be only internal, making sure that mental representations are complete, coherent, and useful. It might also have an external aspect, depending upon the nature of the learning task and environment. For example, learners in formal situations should check to make sure the physical representations of their learning are as attractive as possible and that they conform to the conventions of whatever genre they might represent: Are the grammar, spelling, and punctuation correct? Are the appropriate techniques and conventions of motion media in place? Does the digital presentation use its “bells and whistles” ethically and effectively? Is appropriate credit given for ideas that originated with others? In other words, are all the i’s dotted and t’s crossed? Although it makes its actual appearance only as the fifth stage in the I-LEARN model, “reflect” is actually a central part of the recursive and iterative process that occurs throughout the process of learning in information-rich environments. From the identification of an engaging problem or question and through each of the stages necessary to address that problem or question, reflection should play a key role. The ongoing analysis of one’s progress, revision of ideas and processes in light of developing understandings, and refinement of one’s learning into sensible mental representations and effective communicative structures are essential contributors to active, dynamic learning. Revisiting this process toward the conclusion of a particular learning experience is important as well, but it is not the only time reflection occurs.

5.4.6 Stage 6: kNow Learning results in knowledge, and so the final stage of I-LEARN (displayed in Fig. 5.14) emphasizes that result as well. In this stage, the learner “kNows” what has been learned—that is, he or she instantiates the learning he or she has achieved by internalizing it, personalizing it, and establishing the basis on which he or she can activate it in the future. In other words, the learner incorporates his or her new learning into an existing cognitive “store.” This store is not a static, permanent, monolithic end state but a complex, dynamic, and interrelated web of content and processes as conceptualized by both information science and instructional design and development as outlined in Chap. 1.

Fig. 5.14 Stage 6: kNow

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To internalize new learning, the learner integrates it with knowledge that is already in place. As with the processes related to the “apply” stage, a description of the specific processes by which the learner accomplishes internalization is better left to cognitive scientists than to I-LEARN. However, in broad terms, the process seems to involve the process of equilibration described by Piaget (1952)—whose ideas presaged the theories underlying the constructivist movement. The result is a broadening and deepening of the learner’s understanding of the topic itself and perhaps of its interrelationships to other topics. An increased understanding of the culture and geography of Afghanistan, for example, is likely to enrich either a formal or an informal learner’s understanding of that country’s political history. Personalizing the learning harkens back to the generate component of the “apply” stage, which emphasizes that learning is a highly personal task: each learner relates pieces of information in an individual way in order to construct his or her own personal understanding of the topic at hand. At this point, I-LEARN emphasizes the learner’s realization that his or her learning is a personal construct, not a universal one, and his or her acceptance of the implications of that stance. For example, a student’s understanding of King Lear is likely to be quite different from a professor’s, a director’s, or an actor’s understanding of the play—all of which, in turn, are likely to be quite different from one another. Granting that learning is a highly personal construct allows room for additional learning that will, once again, deepen and enrich the learner’s understanding. The final component of this stage—activate—brings the model full circle. It assumes that the process of learning with information has occurred and that such learning is available for the learner to use as necessary and/or appropriate in new situations. The learner’s cognitive store has been expanded and enhanced, both in its content and in the strength and intricacy of its structure. Moreover, the new knowledge feeds into the learner’s world view—leaving him or her more sophisticated than before in curiosity about the information-rich environment of the world around us. Ideally, the learner is developing that habit of mind that sees that world as a never-ending source of information to be used to solve problems and answer questions. It is no accident that the last element of the final stage in I-LEARN is also the first element in the model’s first stage.

5.5

Conclusion

The I-LEARN model is designed specifically to describe the use of information as the fundamental building block for learning and to provide both a theoretical context and a practical blueprint for that process. Grounded in decades of theory and research in both information science and instructional development and design, it also incorporates current thinking in the learning sciences. It claims as its closest ancestor the information-literacy model used widely in schools, colleges, and universities and expands that model to encompass both formal and informal learning, from both recorded information and information inherent in the people

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and objects around us. It suggests that the world itself is the ultimate informationrich environment and that it that can support the learning of virtually anything of interest to an individual or a group. Ideally, using the stages of the model—Identify, Locate, Evaluate, Apply, Reflect, and kNow—will help learners to develop a habit of mind that sees the world as an all-encompassing source of information that human beings can access, evaluate, and use to solve problems and improve lives. That habit is the cornerstone of independent, lifelong learning in a world brimming with information and with possibilities.

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Chapter 6

I-LEARN and the Assessment of Learning with Information

Abstract Assessment is a staple of formal learning environments and a useful concept for monitoring learning in informal information-rich environments as well. This chapter surveys the recent history of the assessment movement and positions the I-LEARN model as a framework that is especially well suited to a contemporary assessment of learning with information. The model is consistent with both traditional and current approaches to assessment, its structure lends itself to the design of assessment instruments, and it addresses current and emerging thinking about using information as a tool for learning. Above all, it provides a mechanism for linking learning and assessment in a holistic, authentic, and satisfying experience. Different in tone and structure from the preceding chapters, this final chapter draws together and expands ideas introduced throughout the book. It closes the loop about learning in information-rich environments with a discussion of how I-LEARN can promote and assess such learning not only in today’s information-rich environments but in those of the future as well. Assessment—determining what learners know and have learned—has been a part of formal education since at least the fifth century BC. When Socrates asked questions of his students in Athens, he was, in effect, conducting a kind of formative assessment: that is, he used their initial answers to ascertain their underlying knowledge and then continued questioning to help them correct and expand that knowledge. History does not tell us whether Socrates administered a summative assessment—a final exam—at the end of the process, but we can be confident that he was concerned about his students’ ultimate achievement as well as their progress along the way. Over the following centuries, assessment changed dramatically. Not only has it become far more complex and formalized, it has often become divorced from its origins as a teaching tool. In the twentieth century, summative assessment emerged as one of the primary foci of the modern education establishment. Today, not only assessing what students have learned but also documenting their attainment of specific outcomes at the conclusion of an instructional experience is a leading factor in American (and other) educational policy. High-stakes summative assessment—the D. Neuman, Learning in Information-Rich Environments: I-LEARN and the Construction of Knowledge in the 21st Century, DOI 10.1007/978-1-4419-0579-6_6, © Springer Science+Business Media, LLC 2011

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kind in which test scores are used not only to certify achievement but also to permit advancement or graduation and to determine competitive advantage in further study—is in place around the world. The SAT, ACT, and Advancement Placement exams in the United States; the O-Level (or GSCE) and A-Level exams in most Commonwealth countries; the Matura in much of Eastern Europe; and the Abitur in Germany, Austria, and Finland all exemplify this approach. Some broader conceptions of assessment—that is, conceptions that once again link assessment directly to teaching and learning—are beginning to appear, but the predominant view of assessment early in the twenty-first century still involves very high stakes for faculty and schools as well as for students. Against this background, the question becomes: What role does assessment, both formative and summative, play in today’s information-rich environments, both formal and informal? What is its relevance, its contribution, to the kind of learning discussed in this book? To answer that question, it is critical both to understand key contemporary views of assessment and to explore how they might be adopted or adjusted to meet the needs of today’s learners.

6.1

Evolving Views of Assessment

Contemporary views of assessment are evolving, and a brief review of how they emerged over the past twenty years will set the stage for understanding that evolution and its current status. Arguably, in the United States the most important factor during this period has been the growth of national interest in summative assessment in formal education. Since the National Governors’ Summit first identified educational goals for the country in 1989, this kind of assessment has become the siren song for legions of educators. Scholars, curriculum specialists, and policy makers at all levels have worked along two parallel tracks: (1) to identify concepts and skills that students should master through instructional experiences and (2) to craft tools and measures for assessing that mastery at the end of courses or programs of study. In the 1990s, national professional organizations published hundreds of “standards” that specified such concepts and skills across elementary-, middle-, and secondary-school disciplines (collected in Kendall and Marzano’s 650-page tome published in 2000). State and local educational agencies adapted these national statements to create their own lists of standards that soon constituted their official (or at least virtually official) curricula. Because the statements captured what disciplinary experts had identified was important for students to learn, they were readily transcribed into “scope and sequence” documents that led, in turn, to specific lessons and other curricular materials that drove what students were taught across the country. By and large, the standards reflected in all these developments are summative in nature: that is, they describe the final results, or “outcomes,” of students’ learning rather than the process students might take to achieve these results. The term “outcomes”

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suggests as much. And once such standards/outcomes have been established, assessing students’ achievement of them is the inevitable next step: “Assessment is inherent in the idea of standards. The reason for specifying [them] is to provide statements about what is important for students to learn so that, in turn, instructors can evaluate how well students have achieved those outcomes. Assessment is the intrinsic, unavoidable flip side of standards” (Neuman 2000, p. 111). Not surprisingly, the development of standards was soon followed by the development of instruments to judge students’ achievement of them. Indeed, stating explicit outcomes in a clear and consistent way and using them as the basis for assessment has strong theoretical advantages and has been a cornerstone of instructional design and development for decades. Arguably, in fact, today’s focus on assessment began over fifty years ago, with the publication of Bloom’s original Taxonomy of Educational Objectives in 1956. The taxonomy used “illustrative educational objectives selected from the literature” (p. 201) to suggest assessments at each of the six levels of learning, from “knowledge” to “evaluation,” specified in the Taxonomy. A statement like “The student shall know the methods of attack relevant to the kinds of problems of concern to the social sciences” (p. 203) indicates that outcomes of the kind created in the 1990s were already in place decades earlier. Years of research and experience with the outcomes-assessment approach— guided by early authors like Mager (1962) and Briggs (1977) as well as by Bloom (1956) and many others—have yielded a strong body of theory and practice to guide the creation of an outcomes-instruction-assessment continuum that has many advantages. It forces both instructors and designers to identify the major concepts and skills they want learners to master, helps teachers direct instruction toward those outcomes, and eliminates at least some of the subjectivity from grading. It informs learners of what is most important for them to learn, taking the guesswork out of learning and helping more of them achieve higher levels of understanding. The approach has endured largely because of these advantages. Contemporary authors like Wiggins and McTighe (1998, 2005) have counseled a modified version of assessing students’ performance against specified outcomes through their notion of “backward design”: “starting with the end (the desired results) and then identifying the evidence necessary to determine that the results have been achieved (assessments)” (p. 338). While outside the traditional literatures of both the instructional-design and assessment communities, the “backward design” movement has had widespread influence in the K-12 community in recent years. It was the implementation of No Child Left Behind (signed into law in 2002)—coupled with the emergence of the wave of standards that emerged in the 1990s—that thrust the American interest in summative assessment to a new level. Although standardized tests had been used to evaluate schools and students for decades, the new law’s requirements for high-stakes standardized testing at specific grade levels and in specific subject areas spawned a large and lucrative cottage industry of publishers and others who developed state-level tests that have become the ultimate measure of students’—and schools’—success. During the first decade of the new century, educators, parents, and society at large were using such tests to

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reward and punish schools, to design plans for school improvement, and to reinforce the value of real estate to families who want to live where there are “good schools” (see, for example, Brasington and Haurin 2006, 2009; Haurin and Brasington 1996). For many, this focus on assessment seemed to make Lake Wobegon—“where all the children are above average”—the new American educational Utopia.

6.2

Looking Ahead

In general, No Child Left Behind has been deemed successful at its major goal— reducing disparities in achievement in both math and reading between majority and minority students at all levels of American schooling. The law has many critics, but a national focus on assessing student achievement is sure to persist. One indication of this persistence is the effort to create national “Common Core State Standards, K-12” in mathematics and English language arts. Released in June 2010, the standards had been adopted by over thirty states plus the District of Columbia by the following August (see http://www.corestandards.org/ for up-to-date information.) Sure to be adopted by more states as they complete their formal procedures, the standards specify foundational skills and knowledge that are important for college and workforce training programs across the country. These outcome statements— and the assessments that are sure to follow them—are already broadening the discussion of K-12 assessment. Another example that suggests the persistence of assessment is Microsoft’s Partners in Education Transformation Project. Formed in 2009 with Cisco and Intel, the project offers severe criticism of both traditional schooling and traditional assessment but still promotes assessment as the primary strategy for achieving what it calls “transformative reform.” The project’s “Assessment Call to Action”—one of the first documents it released—calls for “specify[ing] high-priority skills, competencies, and types of understanding that are needed [by] productive and creative workers and citizens of the 21st century and turn[ing] these specifications into measurable standards and an assessment framework” (Assessment Call to Action, p. 2; italics added). The project, along with its influential backers, thus presents assessment as an integral part of broadening our understanding of the kinds of learning that are most important today and of dealing with them in a holistic manner. Perhaps most significantly, the project interweaves learning and assessment into its image of comprehensive reform (see http://www.microsoft.com/education/ programs/transformation.mspx). A parallel yet very different development emerged early in the new century, when associations that accredit colleges and universities and the individual programs they offer started to focus on what students actually learned in these venues rather than only on what resources were brought to bear on their educations. Two quite different associations exemplify the range of this effort: the American Library Association (http://www.ala.org/), which accredits only Master of Library Science

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programs within higher education, and the Middle States Commission on Higher Education (http://www.msche.org/), which accredits entire degree-granting colleges and universities in “Delaware, the District of Columbia, Maryland, New Jersey, New York, Pennsylvania, Puerto Rico, the U.S. Virgin Islands, and several locations internationally.” Both organizations—and others as well—now require applicants for accreditation to specify learning outcomes for their students and to demonstrate that programs do in fact lead students to these outcomes. In many cases, the movement of this focus on learning assessment into higher education brought to a new audience the idea of formally identifying learning outcomes both within courses and across programs. Fueled not only by accreditation agencies but also by a public—and some state legislatures—wondering if higher education is worth its increasingly higher cost, the focus on outcomes and outcomes assessment continues to grow across postsecondary education. Despite concerns that the approach brushes up against the tradition of academic freedom (at least in part because specifying outcomes implies organizing the curriculum around them), individual faculty and their institutions are now writing learning outcomes much as their K-12 colleagues do. Clearly, the idea of learning assessment has thoroughly penetrated the realm of formal education.

6.3

Assessment and Learning with Information

Educators concerned with the use of information for learning did not escape the standards-and-assessments wave of the 1990s. As noted in Chap. 4, several national organizations developed information-literacy standards and information-technology standards for both K-12 and postsecondary audiences. Information Power: Building Partnerships for Learning (American Association of School Librarians and Association for Educational Communications and Technology 1998) and Objectives for Information Literacy Instruction: A Model Statement for Academic Librarians (Association of College and Research Libraries 2001) covered the information literacy landscape in formal education. The original National Education Technology Standards (NETS) (International Society for Technology in Education 1998) covered the information technology landscape for K-12 students; in subsequent years, the NETS for students have been revised (2007), while NETS for teachers (2000, 2008) and administrators (2002, 2009) have been added to the mix. Once again, Bloom’s Taxonomy (1956) was called upon to guide the creation of outcome statements in this arena as well: an outcome such as “Judges the accuracy, relevance, and completeness of sources and information” (American Association of School Librarians and Association for Educational Communications and Technology, p. 14) clearly exemplifies the use of the Taxonomy by targeting learning in the domain of information literacy at the “evaluate” level. The standards-and-assessment wave—particularly in regard to using information—began to alter its course in the early part of the new century with a pair of initiatives developed in tandem. In 2003, the Partnership for 21st Century

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Skills (www.21stcenturyskills.org) produced Learning for the 21st Century: A Report and MILE (Milestones in Learning and Education) Guide for 21st Century Skills; in the same year, the Educational Testing Service (ETS) (www.ets.org) released a white paper entitled Succeeding in the 21st Century: What Higher Education Must Do to Address the Gap in Information and Communication Technology Proficiencies. Like the statements of essential information-literacy competencies and skills developed by these initiatives and introduced in this book in Chap. 4, these “assessment” documents offered a welcome new focus to those concerned with learning in information-rich environments. Both documents broke with the previous decade’s focus on subject-area standards to focus on establishing cross-disciplinary standards and assessments related to what are now called “information and communication technologies,” or the ICTs delineated in this book in Chap. 3. “ICT literacy,” which puts information at the core of learning, was defined as: the ability to use digital technology, communication tools, and/or networks appropriately to solve information problems in order to function in an information society. This includes the ability to use technology as a tool to research, organize, evaluate and communicate information and the possession of a fundamental understanding of the ethical/legal issues surrounding the access and use of information. (Educational Testing Service 2003, p. 11)

Subsequent efforts at ETS spawned the development of an ICT Literacy Assessment at the postsecondary level, which is based on ETS’s seven components of ICT literacy: defining an information need, accessing information, managing information, integrating information from multiple sources, evaluating information, creating new information, and communicating information. This assessment joined the bank of assessments in a variety of areas that ETS has been building for sixty years, giving learning with information a new status in the postsecondary world. The ICT Literacy Assessment in turn evolved into an iSkills ™ research and assessment program, which continues to focus on these components and to develop a more comprehensive approach to understanding and assessing them. (See, for example, white papers produced for ETS by Tyler 2005 and Katz 2005, 2007.) Subsequent efforts of the Partnership for 21st Century Skills led to the publication of its Framework for 21st Century Learning in 2004. This document, supported by some forty organizational “partners,” specifically suggests a holistic view that links learning and assessment and offers “a unified, collective vision for 21st century learning [italics added] that will strengthen American education” across the board. As noted in Chap. 4, the Framework includes eleven “core subjects” (traditional curricular categories like language arts and science) and four “21st century themes,” including such topics as “global awareness” and “civic literacy.” Most significantly for learning with information, the document offers three sets of skills that support students’ mastery of each of those fifteen core subjects and contemporary themes: “learning and innovation skills,” “life and career skills,” and “information, media, and technology skills.” The Framework’s marriage of “information” skills and “media and technology skills” bridges ideas inherent in earlier sets of informationtechnology and information-literacy standards noted above. And through its identification of “information, media, and technology skills” as necessary for mastering all

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the subjects and themes, the Framework also moves learning with information—and assessing such learning—into a key position in its “holistic view.” Not surprisingly, the concepts and language of the Framework for 21st Century Learning are embedded in the new Microsoft/Cisco/Intel Partners in Education Transformation Project described above: Microsoft, Cisco, and Intel are all among the approximately forty “member organizations” that support the Partnership for 21st Century Skills. Like these three, a number of the partner organizations are international in scope, suggesting that the Microsoft/Cisco/Intel project’s influence will not be limited to North America. In fact, this initiative states that it has formal plans to “examine innovative ICT-enabled classroom-based learning environments and formative assessments that address 21st century skills and draw implications for ICT-based international summative assessments and for reformed classroom practices aligned with assessment reform” (Assessment Call to Action 2009, p. 2). Calling specifically for assessment reform that will drive instruction to focus on learners’ mastery of the information skills that are at the heart of ICT literacy, this initiative provides perhaps the most striking contemporary imperative for assessing learning with information.

6.4

I -LEARN and Assessing Learning with Information: Formal Environments

Whether we look to the standards that undergird most educational practice today or to the alternatives suggested by the Educational Testing Service and the Partnership for 21st Century Skills, we find no lack of outcome statements that can be used to describe and assess what it means to use information as a tool for learning. And in formal educational environments—still generally organized by disciplinary categories and held accountable for students’ mastery of those categories—the clear statement of outcomes and the development of instruments to assess mastery of them is an approach that is likely to remain no matter what statements are adopted. Whether viewed as holistic or discrete, learning—including learning with information—will continue to be defined at least in part by outcome statements. Within formal settings, the I-LEARN model explained in Chap. 5 provides a useful scaffold for assessing students’ ability to use information as a tool for learning. Grounded in learning theory, tied to the structure of information literacy, and linked both conceptually and practically to Anderson and Krathwohl’s (2001) update of Bloom’s original Taxonomy of Educational Objectives (1956), the model is situated in traditional ideas of learning and assessment but expands them to encompass newer approaches as well. As shown in Fig. 6.1, it includes six stages and eighteen elements drawn directly from the theory and practice of learning with information and provides a framework for assessing such learning as well as fostering it. For example, the model’s first five stages state specific outcomes that can be readily assessed through corresponding evaluation items: the learner will Identify a problem, Locate information about it, Evaluate the information according to specific

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Fig. 6.1 I-LEARN stages and elements

criteria, Apply the appropriate information to construct knowledge, and Reflect on the process and product of that construction. The last stage—kNowing what has been learned—is not directly assessable but speaks to the holistic nature of learning and to the model’s ultimate step of internalizing kNowledge so that it can be used in the future. Overall, the model provides a guide for creating and implementing assessment across the full continuum of possible information-literacy outcomes; its breadth and flexibility allow its application for both formative and summative assessment. At the “identify” stage, for example, learners can be assessed on the degree to which they generate a problem or question that is substantive and information-based: a question that taps learning that requires remembering factual knowledge—the party affiliation of one’s local congressional representative, for example—would be less impressive than one that taps learning that requires metacognitive knowledge— perhaps analyzing the electoral process at the state or national level. Similarly, at the “apply” stage, generating a new (to the learner) understanding of the definition of the Chi-square statistic—conceptual knowledge—would be less impressive than generating a new understanding of how to apply the Chi-square test to a particular statistical problem—which requires, at the very least, procedural knowledge. Variations on this scaffold are, of course, almost infinite—enabled and constrained by learners’ needs, teachers’ abilities, curricular goals, whether the assessment is formative or summative, and a host of additional conditions and circumstances. Nevertheless, the possibilities suggested by the model’s links to Anderson and Krathwohl’s (2001) types of knowledge and levels of learning as these relate to information provide the basis for an intriguing assessment tapestry. Even without investigating such a tapestry, it is clear that I-LEARN’s stages and elements for learning with information could readily be assessed by conventional strategies, be they test items or checklists or criteria on a rubric. Morrison et al. (2004) identify a dozen or more assessment tools that might be used to evaluate various aspects of learners’ ability to use information to learn: multiple choice, true/ false, matching, short-answer, and essay tests as well as checklists, performance ratings, problem-solving exercises, and rubrics. A multiple-choice item, for example, might require learners to “identify,” within an array of choices, the best example of a question that can be answered with information; a problem-solving exercise might require them to “locate” appropriate sources to answer an information-based question. As in other subject areas, a test bank of such items could be developed, administered, and graded for students at any level.


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Harada and Yoshina (2005) add personal conferences, activity logs, personal correspondence, and graphic organizers like concept maps, idea webs, K-W-L charts, and matrices to the mix of tools for assessing learning with information. Such approaches lend themselves especially well to formative assessment but can be used in summative assessments as well. In terms of I-LEARN, they might involve learners in creating matrices comparing their “evaluations” of a variety of information sources according to criteria learned in a class—for example, authority, relevance, and timeliness as these facets pertain to a particular project. Or it might involve creating concept maps or idea webs that show the results of their “application” of information to answer a question—the relationships among climate, topography, and altitude on exports from Brazil, for example—along with citations to the sources used to find that information. Overall, Harada and Yoshina’s (2005) advocacy for using visual displays as assessment tools offers an intriguing option to more traditional assessment approaches. These authors suggest a number of ways that visual displays could be used to assess students’ use of information as a tool for learning. One of the most popular assessment tools today, in both K-12 and higher education, is the rubric—an instrument in the form of a grid that identifies the components of a task, the criteria for assessing the quality of each completed component, and scores that correspond to the instructor’s judgment about a learner’s level of performance related to those criteria (Strickland and Strickland 2000). Arguably, the rubric is also the most promising tool for assessing students’ ability to learn with information in a formal setting. Rubrics’ inherent connection to the process of learning and their strength in addressing both that process and its outcome make it ideal for assessing what is essentially process-based learning: the process of using information to generate knowledge. Giving a learner a rubric in advance allows that learner to see specifically what is expected, to work toward that expectation, and to determine for him- or herself the degree of success attained. A rubric also allows for iterative formative assessment and enables an instructor to provide targeted feedback to a learner by explaining how that learner excelled or fell short in a particular area. Ultimately, then, a rubric allows an instructor to provide guidance for improving both the process and the outcome of learning. Using rubrics is thus fully consistent with both formative assessment, whose goal is improved understanding and performance, and summative assessment, whose goal is to document the outcome of the learning process. As Harada and Yoshina (2005) note, “A well-designed rubric is both a tool for assessment and a powerful teaching strategy” (pp. 21–22). Fig. 6.2 illustrates a generic rubric that might be adapted to any subject area to evaluate students’ understanding of each of the stages of learning with information outlined in I-LEARN. Assessing a learner’s achievement at each step would provide information about how well he or she grasped the pieces of the process, while assessing the learner’s ability to make links across these steps would provide information about his or her understanding of the overall process of learning with information. The assessment might be formative (judging how well students master each step and providing guidance where needed) and/or summative (judging students’ “final” levels of understanding of each step and of the overall process).

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Fig. 6.2 I-LEARN assessment rubric

The range of possible scores—from a high of 20 for a student who scores a 4 for each step to a low of 5 for a student who is unsuccessful at each—provides ample room for a teacher to provide nuanced feedback that would tell a student how well he or she performed at each stage and element and what components of learning with information need additional attention. Like any rubric, this one can be tweaked to reflect the content of the learning experience at hand. For example, it could reflect the difference in “timeliness” when evaluating a learner’s use of information in relation to a historical event like


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the Vietnam War and that same student’s use of information for a report on the contemporary issue of climate change. Similarly, it could be tweaked to reflect the particular resources providing the information: adding a requirement that students look beyond the first three “hits” provided by a search engine’s weighting algorithm would be useful in some settings but not in others. It could include a focus on how effectively students incorporated the learning affordances associated with their final information products (see Chaps. 2 and 3). And, of course, it could be tweaked to reflect an individual teacher’s understanding of particular students’ abilities and needs: the kind of representation or information product expected of middle schoolers would obviously differ from the kind expected of graduating seniors. While there is still much to be learned about using I-LEARN in practice, Fig. 6.2 provides the scaffolding for one of the tools that might be developed to support its use for both formative and summative assessment in schools.

6.4.1 A Curriculum for Learning with Information? Formal assessment is usually related to a formal curriculum, and the question of whether there should be an “information literacy” curriculum surfaces regularly within the research and professional community of school librarians and library media specialists. Conventional wisdom—buttressed by extensive research (see, e.g., Eisenberg et al. 2004; Kuhlthau 1987; Loertscher and Wools 2002)—holds that information-skills instruction should be integrated with instruction in subject areas so that it is meaningful to students and so that they will remember it from year to year. Even if the new focus on ICTs elevates instruction in this area to curricular status, such instruction would have to be related to—if not anchored in—other subject-matter areas. I-LEARN lends itself to integrated instruction because of its general nature and because of its emphasis on process rather than only on outcomes. Fig. 6.3 shows how instruction based on the model might be integrated with curricular content to teach students not only that content but also the knowledge and skills to use information as a tool for mastering it. The figure also suggests the model’s utility as a way to link instruction in using information as a tool for learning and the assessment of students’ achievement. This structure—which suggests both curriculum and assessment—lends itself not only to K-12 use but to higher education environments as well. Since the release of Objectives for Information Literacy Instruction: A Model Statement for Academic Librarians (Association of College and Research Libraries) in 2001, college and university libraries have come under increasing pressure to demonstrate their value by showing, among other things, a connection to student learning. In recent years, the Association of College and Research Libraries has released a series of documents designed to guide instruction in how to use the library and its resources and in how to conduct library-based research: Information Literacy Standards for Science and Technology (2006), Research Competency Guidelines for Literatures

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Fig. 6.3 I-LEARN and formal instruction: A seventh-grade social-studies activity

in English (2007), and Information Literacy Standards for Anthropology and Sociology Students (2008) (all available at http://www/ala.org/ala/mgrps/divs/acrl/ standards/infolit.cfm). Constructing modules for students in different majors according to the I-LEARN scaffold could provide an efficient and effective approach to helping undergraduates gain the information skills they need. Modules related to questions about particular issues in chemistry, in Russian literature in translation, in ethnographic methods, etc., could help students wrestle with areas their instructors identify as important as well as mastering the information skills required to do research in those areas. Collaboration among faculty, librarians—and students themselves— could lead to rich and enduring experiences of learning sophisticated concepts related to complex curricular topics and high-level information skills. Teaching the

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stages and elements of the model explicitly would give students a tool that could support their learning with information within and beyond the curriculum. Using that tool to guide assessment would create a link between learning and assessment that could result in a holistic and authentic experience for learners.

6.5

I -LEARN and Assessing Learning with Information: Informal Environments

There’s no denying the continuing prominence of assessment in formal learning environments—in the United States and around the world. For both pedagogical and political reasons, assessment is here to stay. Even the Microsoft/Intel/Cisco Partners in Education Transformation Project—arguably one of the harshest critics of contemporary assessment models—wants to “transform” these models rather than eliminating them entirely: “Assessment reform is key to the transformation of the educational system as a whole” (Assessment Call to Action 2009, p. 5). Transforming the educational system may be a worthy goal, but it overlooks the vast amount of learning that occurs outside that system. Informal information-rich environments like public libraries, museums, movie theaters, and the Internet / Web provide tremendous opportunities for learning—and for failing to learn. The patron who cannot navigate the library’s collection, the visitor who fails to recognize the context of a particular museum display, and the movie-goer who hasn’t mastered at least a few film conventions (Salomon 1979) all truncate their opportunities for learning within those venues. Perhaps most importantly, the Web user who doesn’t recognize a world beyond Google and/or Wikipedia misses a virtual world of opportunity to locate, evaluate, and use high-quality information. The effects can range from the simple to the critical: the unskilled movie viewer who doesn’t understand Alfred Hitchcock’s “in joke” of appearing in almost all his movies might miss a moment of pleasure, but the unskilled Internet / Web user who doesn’t understand the importance of evaluating information for authority might make a fatal choice about health care. And just as learning continues well beyond the educational system, so should the assessment of that learning—especially when assessment is defined as an integral part of the learning process. In fact, the need for self-assessment is even greater for “information learners” in informal environments precisely because such environments do not directly support learning with curricular categories, instructional materials, teachers, and school librarians. Learners themselves bear the responsibility for judging and augmenting their own abilities to create knowledge. They take no tests and answer to no authorities. They are the designers and assessors of their own abilities to use information as a tool for learning. Of course, the kinds of assessment that are useful in informal information-rich environments are markedly different from the standard assessments that drive much of formal education: when both the content and the “audience” for these assessments shift from the purview of others to the realm of personal responsibility, tools

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for assessment must be seen in a very different light. Here, too, I-LEARN offers an opportunity to assess—and improve—one’s ability to use information as a tool for learning. Simply invoking the six stages as a mnemonic can remind informal learners of the kinds of concepts and skills that are important in learning with information. Calling into play at least some of the specific elements within these six stages can also enhance such users’ success as learners. Fig. 6.4 provides an example of how the informal learning-and-assessment process might work at an exhibit in a museum, while Fig. 6.5 suggests how it might work during a Web search. The last entry for each stage—assessment—illustrates how the rubric presented in Fig. 6.3 can be applied with both examples.

Fig. 6.4 I-LEARN and informal learning: A trip to a museum

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Fig. 6.5 I-LEARN and informal learning: Learning with the World Wide Web

Of course, the “museum” description above is artificial—no World War II buff or neophyte is likely to proceed exactly according to the steps presented. But I-LEARN provides a basic structure for getting the most learning from an encounter in the information-rich environment provided by museums, and adopting it as a tool can help users maximize their experience. Using it as a checklist to guide a trip to an exhibit can alert learners to ways to enhance their learning, and even using it as an after-the-experience reminder can help them consolidate that learning. The “World Wide Web” example is also somewhat artificial in that it describes the process of learning with information in a linear, dispassionate way. When a user’s “activation” is the result of a troubling event like the diagnosis of a major disease, his or her pursuit of information about the disease is likely to be more

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random than systematic. In instances like this, I-LEARN might also be more useful after the fact, as a checklist to assure the information seeker that his or her information gathering has covered all the appropriate steps and led to warranted conclusions. Both Fig. 6.4 and Fig. 6.5 offer suggestions for applying I-LEARN to informationbased learning in informal situations. Not every situation, obviously, lends itself fully to this approach: a cell-phone tour of Philadelphia’s Elfreth’s Alley, for example, might be more rewarding if a learner tried to understand the variety of concepts presented—architecture, waves of immigration, varieties of occupations, etc.—rather than focusing on only one problem or question. Even here, however, I-LEARN might prove useful for helping a visitor make conscious use of the information at hand to develop meaning from the experience—sorting out the ideas presented in order to generate a personal interpretation to add to his or her store of knowledge and assessing the degree to which his or her learning fulfilled an interest or need. Above all, having I-LEARN available as a tool in such experiences will reinforce a habit of mind that sees the world itself as an information-rich environment and even everyday experiences as valuable opportunities for learning.

6.6

Conclusion

Over several preceding decades, the belief that the essence of learning could be captured in both broad and narrow outcome statements became rooted in the educational establishment. The outcomes-instruction-assessment continuum envisioned in the 1950s evolved over the years into an approach that often granted assessment independence from its pedagogical roots and elevated it to a high-stakes arbiter both of what students must learn in their schools and of what additional formal learning they could pursue after graduation. Until recently, discussions of the role of assessment in learning with information have been largely peripheral—as parents, educators, students, and governments have focused on gauging students’ mastery of traditional subject-matter skills. While that focus is sure to continue, recent developments suggest that society is beginning to understand the importance of specifying the knowledge and skills involved in using information as a tool for learning and, subsequently, of designing assessments to address those outcomes. Against this backdrop, the question arises of how to assess learners’ abilities to use information for learning across a variety of information-rich environments, both formal and informal, and how to design those assessments as pedagogical tools as well as tools for determining mastery of the processes and outcomes of learning. Thinking about the I-LEARN model as a framework for designing assessments yields a variety of general ideas as well as some specific tools that could serve both learning and assessment. I-LEARN’s grounding in contemporary learning theory and in Anderson and Krathwohl’s (2001) recent update of Bloom et al.’s Taxonomy of Educational Objectives (1956) bridges the old and the new to suggest both instructional approaches and ways to design formative and summative evaluations to assess both the process and the outcomes of learning.

References

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Of particular interest in formal environments is I-LEARN’s potential as a pedagogical tool that links learning and assessment (see Phillips and Wong 2010). Its iterative, process-based nature provides a mechanism both for guiding students through the process of learning with information and for ascertaining their understanding of the entire process as well as of its various components. While each of its stages is discrete enough to allow assessment, its special value at this point in formal education might well be the support it provides for helping teachers and students move formatively from stage to stage. Using the model to help learners build upon, correct, and expand their understanding—a la Socrates in Athens— holds promise for helping students truly understand how to use information as a tool for learning. Unlike assessment in formal learning environments, assessment in informal learning environments is always formative, never summative. Its purpose is solely to evaluate one’s own learning and to improve it as much as possible. There is no Socrates sitting in the stoa with informal learners, guiding their progress, but his shade hovers over such learners as they continually question their own understanding and make efforts to improve their knowledge. Using I-LEARN as a self-directed learning tool and for self-assessment can help learners gain the most from their experiences in all the information-rich environments that present themselves as opportunities for learning.

References American Association of School Librarians and Association for Educational Communications and Technology (1998). Information power: Building partnerships for learning. Chicago: ALA Editions. Anderson, L.W., & Krathwohl, D. R. (Eds.) (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s Taxonomy of Educational Objectives. New York: Addison Wesley Longman. Association of College and Research Libraries (2008). Information literacy standards for anthropology and sociology students. Available at http://www.ala.org/ala/mgrps/divs/acrl/standards/ infolit.cfm Association of College and Research Libraries (2006). Information literacy standards for science and technology. Available at http://www.ala.org/ala/mgrps/divs/acrl/standards/infolit.cfm Association of College and Research Libraries (2001). Objectives for information literacy instruction: A model statement for academic librarians. Available at http://www.ala.org/ala/mgrps/ divs/acrl/standards/infolit.cfm Association of College and Research Libraries (2007). Research competency guidelines for literatures in English. Available at http://www.ala.org/ala/mgrps/divs/acrl/standards/infolit.cfm Bloom, B. S. (Ed). (1956). Taxonomy of educational objectives: Cognitive domain. New York: Longman. Brasington, D., & Haurin, D. R. (2006). Educational outcomes and house values: A test of the value added approach. Journal of Regional Science, 56, 245–268. Brasington, D., & Haurin, D. R. (2009). Parents, peers, or school inputs: Which components of school outcomes are capitalized into house value? Regional Science and Urban Economics, 39(5), 523–529. Briggs, L. J. (1977). Instructional design: Principles and applications. Englewood Cliffs, NJ: Educational Technology Publications.

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Educational Testing Service (2003). Succeeding in the 21st century: What higher education must do to address the gap in information and communication technology proficiencies. Available at http://www.ets.org/ Eisenberg, M. B., Lowe, C. A., & Spitzer, K. L. (2004). Information literacy: Essential skills for the information age. Westport, CN: Libraries Unlimited. Harada, V. H., & Yoshina, J. M. (2005). Assessing learning: Librarians and teachers as partners. Westport, CN: Libraries Unlimited. Haurin, D. R., & Brasington, D. (1996). School quality and real house process: Intra- and interjurisdictional effects. Journal of Housing Economics, 5(4), 351–368. International Society for Technology in Education. (1998, 2007). National education technology standards for students. Available at http://www.iste.org International Society for Technology in Education. (2000, 2008). National education technology standards for teachers. Available at http://www.iste.org International Society for Technology in Education. (2002, 2009). National education technology standards for administrators. Available at http://www.iste.org Katz, I. R. (2005). Beyond technical competence: Literacy in information and communication technology. White paper for the Educational Testing Service. Available at http://www.ets.org/ Katz, I. R. (2007). Testing information literacy in digital environments: ETS’s iSkills assessment. White paper for the Educational Testing Service. Available at http://www.ets.org/ Kendall, J. S., & Marzano, R. J. (2000). Content knowledge: A compendium of standards and benchmarks for K-12 education (2nd ed.). Aurora, CO, and Alexandria, VA: Mid-continent Regional Education Laboratory and Association for Supervision and Curriculum Development. Kuhlthau, C. C. (1987). Information skills for an information society: A review of research. Syracuse, NY: ERIC Clearinghouse on Information Resources. Loertscher, D. V., & Wools, B. (2002). Information literacy: A review of the research. San Jose, CA: Hi Willow. Mager, R. F. (1962) Preparing objectives for programmed instruction. Belmont, CA: Fearron. Morrison, G. R., Ross, S. M., & Kemp, J. E. (2004). Designing effective instruction (4th ed.). New York: Wiley. Neuman, D. (2000). Information Power and assessment: The other side of the standards coin. In R. M. Branch & M.A. Fitzgerald (Eds.). Educational media and technology yearbook 2000 (pp. 110–119). Englewood, CO: Libraries Unlimited. Partnership for 21st Century Skills (2003). Learning for the 21st century: A report and MILE guide for 21st century skills. Available at www.21stcenturyskills.org Partnership for 21st Century Skills (2004). Framework for 21st century learning. Available at www.21stcenturyskills.org Partners in Education Transformation Project (2009). Assessment call to action. Available at http://www.microsoft.com/education/programs/transformation.mspx Phillips, V., & Wong, C. (2010). Tying together the common core of standards, instruction, and assessment. Phi Delta Kappan, 91(5), 37–42. Salomon, G. (1979). Interaction of meaning, cognition, and learning. An exploration of how symbolic forms cultivate mental skills and affect knowledge acquisition. San Francisco: Jossey-Bass. Strickland, K., & Strickland, J. (2000). Making assessment elementary. Portsmouth, NH: Heinemann. Tyler, L. (2005). ICT literacy: Equipping students to succeed in an information-rich, technologybased society. White paper for the Educational Testing Service. Available at http://www.ets.org/ Wiggins, G., & McTighe, J. (1998, 2005). Understanding by design. Alexandria, VA: Association for Supervision and Curriculum Development.

Author Index

A Anderson, L.W., 5, 7, 9, 11, 19, 22–24, 27–29, 33–35, 41, 44, 47, 49–55, 61, 76, 90, 91, 93, 96, 106, 123, 124, 132 Anglin, G.J., 21 Aversa, E., 61 Azevedo, R., 43, 76 B Bates, M., 61 Behrens, S.J., 62 Bilal, D., 61 Bloom, B.S., 5, 7, 61, 62, 90, 93, 119, 121, 123, 132 Borgman, C.L., 74 Bransford, J.D., 10, 11, 29, 35, 90, 106 Briggs, L.J., 89, 119 Buckland, M., 3, 9, 90 Buettner, D., 48, 49 C Callison, D., 61 Chung, J., 61 Clark, R.C., 19, 24 Clink, K., 60 Combes, B., 60 Crane, B., 61 Cromley, J., 76

E Eisenberg, M.B., 12, 60, 62, 72, 127 Ellis, D., 71 F Ferreira, S.M., 65, 75 Fidel, R., 61 Ford, N., 12 G Gagne, R.M., 4, 5, 90 Gall, J.E., 69 Gaver, M., 61 Gee, J.P., 30 Giannetti, L., 26 Gordon, C., 62 Gredler, M.E., 28 Gross, M., 71, 88, 99 H Hannafin, M.J., 12, 31, 32, 62, 76, 90 Harada, V.H., 125 Haystead, M.W., 30 Head, A.J., 60 Hill, J.R., 12, 43, 45, 49, 54, 62, 90 J Jonassen, D.H., 41, 77–79, 107

D Daniels, A., 61 Dede, C., 41, 43, 59 Dervin, B., 3, 12, 71, 90, 105 Dewey, J., 110 Didier, E.K., 61

K Kafai, Y., 61 Kozma, R.B., 19–21, 24, 25, 29, 41, 45, 49, 54, 69, 72

D. Neuman, Learning in Information-Rich Environments: I-LEARN and the Construction of Knowledge in the 21st Century, DOI 10.1007/978-1-4419-0579-6, © Springer Science+Business Media, LLC 2011

135

136 Krathwohl, D.R., 5, 7, 9, 11, 19, 22–24, 27, 29, 33–35, 41, 44, 47, 49–55, 61, 76, 90, 91, 93, 96, 106, 123, 124, 132 Kuhlthau, C.C., 3, 12, 62, 71, 88–90, 99, 105, 127 L Landow, G., 52 Land, S., 31, 32 Large, A., 61, 74 Lathrop, A., 61 Leckie, G.J., 71 Lee, H.W., 26, 76 Lim, C.P., 76 Lohr, L.L., 69 M Mager, R.F., 119 Mancall, J.C., 61 Marchionini, G., 3, 9, 11, 19, 23, 27, 29, 33, 35, 41, 61, 71, 74, 90 Markowitz, N.L., 61 Marzano, R.J., 30, 118 Mayer, R., 12, 20, 90 McCandless, D., 1 McClure, R., 60 McGregor, J.H., 62 McTighe, J., 88, 119 Merrill, M.D., 4, 5, 90 Morrison, G.R., 124 N Neuman, D., 12, 61–63, 65, 70, 75, 103, 104, 108, 119 O Oh, E., 59 Oliver, K., 31 Oliver, R., 65, 74

Author Index P Paivio, A., 21 Perzylo, L., 65, 74 Piaget, J., 10, 113 Pitts, J.M., 62, 71, 103 R Reagan, R., 1 Reeves, T.C., 59 Rich, M., 20 Rieber, L.P., 31 Ross, B., 20, 21 S Salomon, G., 25, 26, 45, 69, 89, 103, 129 Saxton, M.L., 88, 99 Schon, D., 110 Seels, B., 4, 25 Smaldino, S.E., 21, 27, 68 Small, R.V., 12, 62, 64, 65, 75, 89 Soergel, D., 3, 6 Spink, A., 3, 71 Squire, K., 30 T Tay, L.Y., 76 Todd, R., 62 V Valenza, J., 72 Vygotsky, L.S., 23, 89 W Wiggins, G., 88, 119 Wilson, T.D., 13, 71, 90 Wozny, L.A., 61 Y Yoshina, J.M., 125

Subject Index

A Access to information objects, 42–43 Activate (element in I-LEARN model), 113 Affective learning, 89 Alexandria Proclamation, 86 American Association of School Librarians, 86 American Library Association, 63, 86 Analyze (element in I-LEARN model), 110 Analyze (from the cognitive process dimension), 7, 41, 93, 94, 96 Apply (stage in I-LEARN model), 105–109, 123, 124 Apps, 40, 42, 55 Assessment Call to Action, 120 Assessment tools, 124, 125 Association for Educational Communications and Technology, 121 Association of College and Research Libraries, 63, 86, 127 Auditory information objects, 22–23 Auditory literacy, 68–69 Augmented reality, 55 Authority (element in I-LEARN model), 102 B Backward design, 119 Behaviorism, 10 Big Six, 72 Blended Librarian Online Learning Community, 72 Bloom’s Taxonomy, 5, 61, 62, 91, 119, 121, 123, 132 C Categories of learning analyze, 8, 12, 52, 93, 95 apply, 8, 52, 93, 94

create, 8, 12, 52, 93, 95 evaluate, 8, 12, 52, 93, 95 remember, 8, 52, 93, 94 understand, 8, 52, 93, 94 21st-century learning, 13, 65, 66, 86, 122 21st-century skills, 67, 123 Cognitively relevant characteristics, 19 Cognitive process dimension, 7–9, 76, 93–96 learning levels, 93–95 Cognitive science, 10 Collaboration, 28, 45–50, 64, 70, 77, 128 Collaboration and discourse strategies, 51–54 Collaborative learning, 40, 43, 47, 50, 51 Common Core State Standards, K–12, 120 Communicate (element in I-LEARN model), 108–109 Component display theory, 5 Computer-mediated communication, 40, 50 Computer-mediated learning environments, 31 Computer-supported collaborative learning, 50 Constructivism, 77 Cyberlearning, 59 D Developmental theory, 10 Digital immigrants, 60, 85 Digital information objects, 29–34 Digital literacy, 86 Digital natives, 59, 85 Discourse strategies, 45, 46, 51–54 Discourse strategies and distributed processing, 49–51 Distance learning, 40, 45 Distributed processing, 45–47, 49–51 Distributed processing and collaboration, 46–49 Distributed resources, 45, 53, 54 Dual-trace theory, 21 137

138 Dynamic multisensory information objects, 24–26, 69 Dynamic visuals, 21 E Educational Testing Service (ETS), 66, 70, 121–122 Electronic resources, 61 Embedded knowledge, 44, 52, 76 Equilibration, 112 Essential questions, 88 ETS. See Educational Testing Service Evaluate (stage in I-LEARN model), 101–105, 123 Expressive learning, 23, 24, 35, 46, 47, 51, 53, 54, 68, 70 Extract (element in I-LEARN model), 99–101 F Film, 18, 19, 24–26, 35, 45, 67, 89, 129 Filmic code, 25, 26, 34, 69, 103 Find (element in I-LEARN model), 99, 100 Focus (element in I-LEARN model), 99–101 Formal learning, 22, 35, 51, 98, 107, 108, 129, 132, 133 Formative assessment, 117, 125 Formulate (element in I-LEARN model), 98 Framework for 21st Century Learning, 65–67, 122–123 G Games, 27, 28, 30, 42, 69, 78 Generate (element in I-LEARN model), 108, 112 Generative learning environments, 30 Global information village, 17–18 Google.docs, 50 Graphics, 68, 74, 91 Grid computing, 49 Guided inquiry, 12, 88–89 H Habit of mind, 35, 90, 98, 102, 113 Hypermedia, 40 I ICT literacy, 66, 122 ICT Literacy Assessment, 122

Subject Index ICTs. See Information and communication technologies Identify (stage in I-LEARN model), 97–99 I-LEARN model assessment movement (see Informationliteracy assessment, I-LEARN) model and representation, 87, 88 stages apply, 105–109, 123, 124 evaluate, 101–105, 123 identify, 97–99, 123 kNow, 112–113, 123 locate, 99–101, 123 reflect, 109–112, 123 theory cognitive process dimension, 93–96 information literacy, 96–97 instructional systems design, 90 knowledge dimension, 91–93 Immersive virtual environments, 40 Informal learning, 22, 35, 40, 51, 93, 113, 130–133 Information (definition), 2–4, 11, 14, 85, 86 Information and communication technologies (ICTs), 39–55, 66, 70, 76–78, 122, 123, 127 Information behavior, 13, 71, 73, 86, 87, 89, 97 Information behavior model, 13, 71 Information evaluation, 73 Information formats, 19 Information gathering, 18, 44, 45, 48, 53, 54, 104, 111, 131 Information literacy ALA definition, 63 and cognitive process dimension, 95, 96 concept and outcomes, 62–63 curriculum, 127 definition, 85–86 and I-LEARN, 88 and instruction, 86–87 student learning categories, 64 Information-literacy assessment, I-LEARN curriculum, 127–129 formal environments, 123–127 formative assessment, 117 Framework for 21st Century Learning, 122–123 informal environments, 129–132 No Child Left Behind, 119–120 outcomes-assessment approach, 119 standards-and-assessments wave, 121 summative assessment, 117–119 transformative reform, 120

Subject Index Information Literacy Competency Standards for Higher Education, 63 Information-literacy curriculum, 127–129 Information Literacy Standards for Anthropology and Sociology Students, 63, 127 Information Literacy Standards for Science and Technology, 63 Information objects auditory, 22–23, 68 definition, 18 digital, 29–31 dynamic, 3, 9, 11, 14, 24–26, 33, 44, 54, 90, 103 dynamic multisensory, 24–26, 69 ICT environment, 42–43 learning affordances, 31–34, 103 multisensory, 19, 23–27, 34, 43, 47, 54, 68, 69, 72, 75, 90 nonelectronic interactive, 27–29 single sense, 19–23, 34, 43, 47, 68, 69, 72, 90, 109 static multisensory, 23–24 types, 19 varieties, 18 visual, 20–22, 68 Information Power: Building Partnerships for Learning, 63, 110, 121 Information problem-solving model, 71, 72 Information processing, 9, 10, 71 Information professionals, 2, 3, 60, 70–73, 87, 110 Information-rich environment (definition), 18 Information science, 9, 12, 53, 71, 73, 79, 90, 112 Information Search Process (ISP), 12 Information-seeking, 3, 12, 13, 61, 62, 71, 73, 78, 88, 89, 92, 96–98, 104, 105 Information-seeking model, 13, 71, 105 Information-seeking process, 3, 61, 71, 87 Information studies, 2–3, 12, 13, 43, 60–63, 67, 68, 70–74, 76–78, 88, 90, 102, 103 Information, types, 4–5 Inspiration (product), 76 Instructional design and/or development cognitive process dimension, 7–9, 76, 93–96 Gagne’s hierarchy, information types, 4–5 Internet/Web environment, 69–70 knowledge dimension, 5–8, 90–93 research issues, 74–76 theoretical frameworks, 77–78 visual and auditory literacy, 68

139 Instructional designer, 2, 4, 9, 11, 67, 71, 78, 109 Instructional theory, 5 Instructional transaction theory, 5 Integrated instruction, 127 Intellectual skills, 4 Interactive displays, 28 Interactive information objects, 26–30, 33, 34, 41, 42, 54, 69, 72 Interactivity, 27–31, 33, 41–44, 54, 70 Internalize (element in I-LEARN model), 112 International Federation of Library Associations, 86 International Society for Technology in Education, 86 Internet, 6, 12, 17, 18, 31, 39–43, 45, 49, 51, 61 Internet-based learning, 45, 49 Internet/Web, 18, 20, 30, 40–42, 60, 64, 69, 70, 74–76, 102, 104, 108 iSkills, 122 J Jason Project, 48 Jigsaw model, 47 K Kaiser Family Foundation, 78 Kidspiration (product), 76 kNow (stage in I-LEARN model), 112–113, 123 Knowledge construction, 45, 46, 50, 54, 55, 77 Knowledge dimension, 5–8, 90–93 L Learned capabilities, 4 Learner control, 33 Learning (definition), 11, 14, 86 Learning affordances, 32–35, 79, 97, 109 auditory information objects, 22–23 digital information objects, 29–31 dynamic multisensory information object, 24–26 of ICT Environment, 42–44 learners, 103 theory and research, ICT, 45–53 visual information objects, 20–22 Learning communities, 48, 49 Learning theory, 10–11, 35, 97, 105, 109, 123, 132 Levels of learning, 5, 7–8, 33, 52, 54, 61, 90, 93, 96, 119, 124

140 Librarian, 2, 3, 11, 42, 62, 71, 72, 86, 127–129 Library media specialist, 2, 61, 71, 75, 88, 102, 110, 127 Lifelong learning, 63, 86, 98 Locate (stage in I-LEARN model), 99–101, 123 Logistical affordance, 43, 54 M MacArthur Foundation, 60 Manipulatives, 23 Math Forum, 50 MayaQuest, 48 Media formats, 19, 23 Media literacy, 69 Mental models, 11, 20, 24, 61, 62, 76, 108 MERLOT. See Multimedia educational resource for learning and online teaching Microsoft/Cisco/Intel Partners in Education Transformation Project, 65, 66, 73, 122–123 Microworlds, 30, 31 Middle States Commission on Higher Education, 120 Models, 32, 49, 91, 93 Motion, 24, 40 media, 18, 24–26, 69, 111 motion media-formal features, 25 motion media-production effects, 25 Multimedia educational resource for learning and online teaching ( MERLOT), 43 Multimedia literacy, 86 Multisensory information objects, 23–26, 43, 54, 69, 74 N National Educational Technology Standards for Students, 64 National Forum on Information Literacy, 86 National Governors Summit, 118 National Information Literacy Awareness Month, 86 No Child Left Behind, 119, 120 Nonelectronic interactive information objects, 27–29 O Online communities, 40, 48 Open-ended learning environments, 31

Subject Index Organize (element in I-LEARN model), 108, 109 Outcomes assessment, 119, 121 P Partnership for 21st Century Skills, 65, 70, 77 PASW, 44 Personalize (element in I-LEARN model), 112–113 Prague Declaration, 86 Print information objects, 20, 21, 28 Q Question generation, 73 R Reading, 19–22, 34, 120 Receptive learning, 22–24, 45, 46, 53 Recorded information, 100, 102 Refine (element in I-LEARN model), 111 Reflect (stage in I-LEARN model), 109–112, 123 Reflective learner, 110 Relevance (element in I-LEARN model), 102, 103 Research Competency Guidelines for Literatures in English, 63 Revise (element in I-LEARN model), 111 Rubric, 125–126, 130–131 S Sakai, 46 Scan (element in I-LEARN model), 98 School library and/or media center, 72 School library media research, 62 Search strategies, 61, 71 Second Life, 40 Sense making, 12, 46, 71, 90 SharePoint, 50 Simulations, 27, 28, 33 Single-sense information objects, 19–24 Skype, 49 Social construction of knowledge, 46 Social learning, 89 SouthComb, 52, 53 Stand-alone electronic information objects, 30 Standards, 100, 118, 119 Standards for the 21st-Century Learner, 64

Subject Index Static multisensory information objects, 23–24 Static visuals, 21, 22 Structure, 10, 11, 41, 75, 76, 90, 127 Summative assessment, 117–119, 124, 125 Synthetic learning environments, 13, 104 T Technology literacy, 86 Television, 18, 24, 25, 34, 60, 69, 78, 95 Theoretical framework information studies, 12, 63, 73, 77–78 instructional design and development, 12, 63, 73, 77–78, 119 Timeliness (element in I-LEARN model), 102, 104 Trans-Atlantic Slave Trade Database, 52 Transformative reform, 120 Types of knowledge conceptual, 5, 19, 44, 50, 91–93, 97, 124

141 factual, 5, 8, 27, 28, 44, 91–93, 97, 124 metacognitive, 5, 27, 28, 91, 92, 97 procedural, 5, 91, 92, 97 U UNESCO, 86 V Victorian Web, 51, 52 Visual data analysis, 55 Visual information objects, 20–22, 74 Visual literacy, 69 W Wikipedia, 40, 51, 102, 129 Wimba, 50 World Community Grid, 49 World Wide Web, 12, 17, 18, 30, 39, 42–43, 53, 54, 131

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