Design Research on Learning and Thinking in Educational Settings

Enhancing Intellectual Growth and Functioning

David Dai, David Yun Dai

• Éditeur: Routledge
• Année: 2012
• ISBN:
  9781136956300
• Édition: 1
• Sujet: Education
• Sous-sujet: Education General

The key question this book addresses is how to identify and create optimal conditions for the kind of learning and development that is especially important for effectively functioning in the 21st century. Taking a new approach to this long-debated issue, it looks at how a design research-based science of learning (with its practical models and related design research) can provide insights and integrated models of how human beings actually function and grow in the social dynamics of educational settings with all their affordances and constraints. More specifically:

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• How can specific domains or subject matters be taught for broad intellectual development?

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• How can technology be integrated in enhancing human functioning?

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• How can the social organization of classroom learning be optimized to create social norms for promoting deep intellectual engagement and personal growth?

Part I is concerned with broad conceptual and technical issues regarding cultivating intellectual potential, with a focus on how design research might fill in an important a niche in addressing these issues. Part II presents specific design work in terms of design principles, models, and prototypes.

PART I

Issues, Theories,
and Methods

1

FROM SMART PERSON TO SMART DESIGN

Cultivating Intellectual Potential and Promoting Intellectual Growth through Design Research

David Yun Dai

Education seeks to develop the power and sensibility of the mind. On the one hand, the educational process transmits to the individual some part of the accumulation of knowledge, style, and values that constitutes the culture of a people. In doing so, it shapes the impulses, the consciousness, and the way of life of the individual. But education must also seek to develop the processes of intelligence so that the individual is capable of going beyond the cultural ways of his [her] social world, able to innovate in however modest a way so that he [she] can create an interior culture of his [her] own. (Jerome Bruner, 1966, “After John Dewey, What?”)

From Smart Person to Smart Design

What makes people intelligent? This question is often interpreted to mean what makes some people smarter than others? The entire history of research on intelligence uses what I call the smart person paradigm; that is, intelligence is a property of the individual mind. We can trace the logic through the use of language: if a person acts intelligently, then he or she is intelligent, and we might further infer that he or she possesses high intelligence (see Lohman, 2001). The reification, of course, requires evidential support. Research efforts have abounded in the past century to pin down exactly what makes one more intelligent than others. What I briefly mention in the following section are but a few distinct examples.

The year was 1978. In an effort to understand how intelligence works, Campione and Brown (1978) drew insights from the performance of children with mental retardation. What they found lacking in these children in a “transfer of training” task was “executive control” (including metacognition), which is responsible for generalizing and deploying routines and strategies in new situations. “As retarded children do not spontaneously fill in gaps in training, their performance gives clues to the kinds of ‘gap-filling’ which is automatic, or relatively so, for the more intelligent problem-solver” (pp. 287–288). A similar conclusion was drawn from Borkowski and Peck (1986), based on research comparing gifted and regular children on a metamemory task requiring filling in the gaps left by instruction. They found that gifted children did better with fewer trials in the “gap-filling” task and were able to make a far transfer. Similar work based on the then dominant information processing theory led Resnick and Glaser (1976) to propose a definition of intelligence as the ability to learn in the absence of direct or complete instruction. Indeed, the gap-filling capacity was one of the design principles underlying the Aptitude-Treatment Interaction approach (ATI; Cronbach & Snow, 1977; Snow, 1994). This is but only one version of smart person accounts (see, for example, Carroll, 1993; Cattell, 1971; Jensen, 2001).

Now, fast forward to 2005. In an online chess tournament organized by Placechess.com, two amateur chess players as a team became the final winner, defeating some grand masters on their way to the tournament championship. Secret? They “trained” and used three computers to conduct highly skillful analyses, whereas the grand masters were only equipped with mediocre computer programs. Many lessons can be drawn from this event. The most distinct are technological support (computers doing some highly complex calculations and analyses), collaboration (putting heads together, mutual stimulation and evaluation), executive control (deliberation on multiple sources of information and decision-making), and online and offline learning (reflecting on situations and problem solving). To be sure, the role of intelligence in the two amateur players cannot be discounted, which in a way resembles the executive, metacognitive control in the gap-filling research paradigm. However, there is no doubt that the high-level intellectual performance they demonstrated is not possessed by them individually, surely not a property of their minds, but distributed between the two individuals, between the individuals and their environment (conditions and constraints related to chess games) and tools (computer programs) they used. Indeed, there is even an implicit “design” in the distribution of intelligence: taking advantage of what human beings are good at, and what computer programs are good at (see Kasparov, 2007). This and many other social circumstances led Barab and Plucker (2002) to question: what makes an act intelligent; smart person or smart context?

Comparing the intellectual preoccupations in the 1970s and 1990s or 2000s, one cannot help but notice the changes in zeitgeist. Those leading scholars who used to espouse the smart person paradigm back in the 1970s and 1980s have shifted their focus to context (e.g., Brown, 1997; Glaser, 2000; Resnick, 2010; Snow, 1992). Without denigrating the smart person paradigm, it is indeed high time that we consider the problem of “smart design”: how intelligent acts can be enhanced by deliberate arrangements of person–task transactions and environmental support.

Learning and Intelligence: How the Twin Got Separated and Came Back Together

In Alfred Binet’s original conception as well as its more contemporary rendition (e.g., Carroll, 1997), learning is about making adaptive changes through experience, and intelligence is about the ability to make adaptive changes, and the growing potential to become increasingly more intelligent through learning. It follows that intellectual development and learning should be closely related: intellectual functioning enables effective learning, and learning should facilitate further intellectual growth. A child who has a habit of trying to figure out things will be smarter over time than a child who is used to getting ideas from others. However, while individual differences in cognitive abilities have always been treated as an important determinant of learning (e.g., Ackerman, 1988; Carroll, 1997; Haier, 2001), the history of research on learning seems to have little to do with enhanced intelligence until recently (Ceci & Williams, 1997; Kyllonen, Roberts, & Stankov, 2008; Perkins, 1995). Why is this? One reason is that for a long time intelligence has been considered genetically determined and biologically constitutional; one can gain knowledge through learning, but one’s level of intelligence remains virtually unchanged (see Jensen, 2001). Methodologically, it has to do with the divide between what Cronbach (1957) called two disciplines of psychology. While applied research focuses on psychometric testing and adopts an individual differences approach to the study of intelligence, from Spearman (1904) to more recent efforts (see Carroll, 1993; Deary, 2002), basic research on learning takes a situational approach, aimed at understanding the basic underlying processes and mechanisms, how new responses get strengthened, and gradually become habitual, or how information gets encoded and how it is retrieved for use. Concerns over how active learning enhances an intellectual grasp of matters and achieves its adaptive value in a particular functional context became secondary.

Although Estes (1986) pointed out the role of learning and knowledge in enhanced intellectual functioning, it is not until more recently that intelligence has been conceptualized as contextually bound and developing in nature (Ceci, 1996; Lohman, 1993; Sternberg, 1998). One prominent psychometric theory of intelligence (Cattell, 1971) makes a distinction between crystallized intelligence and fluid intelligence, with the former influenced by learning experiences and supported by knowledge (Hunt, 2008), and the latter more biologically determined and difficult to change. However, recent studies (Jaeggi, Buschkuehl, Jonides, & Perrig, 2008) show that even fluid intelligence can be improved through working memory training. A view of intelligence as distributed between the person, the environment, and tools and resources around, rather than a property of the mind (Pea, 1993; see Gresalfi, Barab, & Sommerfeld, this volume) further opens the door for more contextual, dynamic, and incremental accounts of how intelligence (i.e., the human mind) functions and develops through learning experiences.

In addition to the parting of the “two disciplines of psychology” (Cronbach, 1957), child development research has also witnessed a long separation of learning and intellectual development. While learning is considered an intake of specific content information, which is processed and stored in long-term memory to be retrieved later, intellectual development is considered as having its own preordained structural properties and development, devoid of any content and context dependency; progression in cognitive functions will occur sooner or later, regardless of what kind and duration of learning experiences or input one might have (e.g., Piaget, 2001). This more or less Cartesian view of development of mental functions as separate from embodied experiences has been challenged in recent years (see Fischer & Bidell, 2006; see also Siegler, 2000, for a discussion of learning redux in developmental research). From a micro-developmental point of view, Kuhn (2002) argued that learning so much resembles development in its complexity, organization, multifacetedness, and dynamic quality, that “we now recognize learning to be more like development” (p. 111).

Taken together, a broader conception of learning, thinking, and intellectual development seems in order, which would fully incorporate the normative notion of learning aimed at optimal intellectual development.

Learning as an Intellectual Act, and Learning Outcomes as Intellectual Growth

Psychology has come a long way in realizing that learning and thinking are fundamentally intertwined, and that, to a large extent, learning is about learning to feel, think, and act in a more sophisticated, intelligent way. As Resnick (1987) pointed out, the seemingly simple task of learning to read involves development of higher-order cognitive functions, such as nuanced understandings of the syntactic and topical nature of a text, and the active process of filling in gaps (e.g., making inferences, building coherence), and detecting discrepancies in making meaning out of a text. Likewise, learning of basic mathematics should be treated as an interpretative (i.e., intellectual) enterprise, so that “mathematics is seen as expressions of fundamental regularities and relationships among quantities and physical entities” (Resnick, 1987, p. 12; see also Resnick, 1988), rather than merely a set of computation routines, created by geniuses and meant to be committed to one’s memory. It is time, indeed, to advocate a thinking curriculum (Resnick, 2010) that goes beyond the transmission metaphor of learning and the warehouse model of knowledge (Schank & Cleary, 1995), and integrates what we know about the interplay of knowledge and intelligence to elucidate how knowledge can be built to facilitate good thinking and intellectual growth. A broadened view of learning also includes participation in various domains of social practice, to experience the world in new ways, to form new affiliations with various groups of people who are doing meaningful work, and to gain resources to prepare for future learning (Gee, 2007).

Although theoretical expositions of learning in the emergent learning science are abundant, with many new proposals and renditions (see Sawyer, 2006), the following three principles are particularly in line with a focus on learning as an intellectual act.

(a) Learning is Perspectival

To learn at the intellectual level is to gain new perspectives or broaden one’s intellectual horizon, to feel, think, and talk about a particular topic or act upon a particular class of situations in a more intelligent way (Gee, 2003; Gresalfi et al., this volume). Lampert (1990) distinguished this type of knowledge as knowledge-about, that is, the knowledge of the functionality of a particular method or way of knowing in the larger context of social practice (see also Gee, 2003), which is different from knowledge-of, the knowledge of a particular procedure or concept itself. The perspectival principle also implies that, for a given topic or issue, there likely exist multiple perspectives, each having its own assumptions, logic, and values (Bruner, 1996). The perspectival view of learning highlights the educational value of directing attention and developing sensitivities to various ways of meaning making for adaptive and productive purposes. Affectively, it takes the sequential processes of recognizing, appreciating, and valuing to gain particular perspectives. A child who starts to appreciate a particular way of looking at the world (e.g., through Picasso, Hawkin, or Mother Teresa) is changing his or her mental compass in a fundamental way.

(b) Learning is Instrumental

This principle suggests that learning and doing cannot be separated (Schank & Cleary, 1995); the pursuit of learning always serves some intellectual, practical, and social purposes, be it scientific discovery, engineering a product, or environmental protection. Dewey (1997) put it this way:

Intellectual organization originates and for a time grows as an accompaniment of the organization of acts required to realize an end, not as the result of a direct appeal to thinking power. The need of thinking to accomplish something beyond thinking is more potent than thinking for its own sake. (p. 41)

Therefore, human motivations are always deeply involved in any socially organized, goal-directed learning activities; it is important, therefore, that students feel “a need to know” (Wise & O’Neill, 2009, p. 90). Learning is optimal when the purposes, structure, and tools of a relevant domain of knowledge are made clear to learners, so that they know why to engage in an activity and how to find and use available tools and resources to achieve their goals. The instrumental principle also implies that contents of knowledge need to be connected so that the learner can see how the parts are linked to the whole in a domain in serving larger functional purposes. The metaphor of “learning your way around” (Greeno, 1991; Perkins, 1995) is powerful in explaining how learning as an intellectual act is to build conceptual understandings of the deep structure or “design grammars” (Gee, 2007, p. 28) that serve to organize seemingly discrete factual and procedural information and turn it into “usable knowledge” (Bransford, Brown, and Cocking, 2000, p. 16). While the history of learning theories was replete with atomists who portrayed learning as a linear accumulation of bits and pieces of knowledge in building a whole (see Hilgard, 1948), the navigation metaphor of learning suggests that learning is an act of navigating complex conceptual spaces and understanding how a particular component is connected with other components in the workings of a machinery, a group of people, an ecosystem, so on and so forth, so as to inform our action in a related practical setting. To be sure, honing skills and consolidating procedural and conceptual instruments take much instruction, training, and deliberate practice over time (recall the 10-year rule in the development of expertise; see Ericsson, 2006). It is important, however, to distinguish between technical proficiency and conceptual understanding in skill development. Technical proficiency reflects the kind of procedural competence that works in a fixed way, thus reproductive in nature. Only conceptual understanding can make one’s thinking truly productive in that it enables one to adaptively solve problems for which no ready solution is available (Hatano, 1988). The instrumental view of learning is an antidote to the type of learning that produces inert knowledge, which is a major problem in modern education (Whitehead, 1929). It is the use of knowledge in problem solving that propels extended learning and knowledge building.

(c) Learning is Reflective

Dewey (1933) takes reflective learning as the central task of education: “The real problem of intellectual education is the transformation of more or less casual curiosity and sporadic suggestions into alert, cautious, and thorough inquiry” (p. 181). Learning is re...

Table des matières

• Cover
• Half Title
• Title Page
• Copyright
• Dedication
• Contents
• Preface
• Acknowledgements
• PART I: Issues, Theories, and Methods
• PART II: Models, Tools, and Pragmatics
• Epilogue: Where Are We, and Where Are We Going?
• About the Contributors
• Author Index
• Subject Index