Educational Psychology: Getting to the Heart of ... - Wiley Online Library

APPLIED PSYCHOLOGY: AN INTERNATIONAL REVIEW, 1998,47 (2). 263-283

Educational Psychology: Getting to the Heart of the Matter Through Technology Richard S. Prawat & Valerie L. Worthington Michigan State University, USA Les efforts entrepris aux Etats-Unis et dans d’autres pays pour reformer le systeme Cducatif se perdent dans les sables parce que souvent, pensons-nous, les idees qui sont au coeur de ces riformes ne sont pas correctement appliquees. Les chercheurs ont tendance ?I surestimer la valeur heuristique de leurs idees. Les rkcentes percees technologiques offrent une solution a ce problkme remanent. La technologie donne la possibilite d e mettre 2 I’epreuve de faqon systkmatique des dimensions du processus d’apprentissage qui pourraient autrement rester noyees dans un ocean de details, deux exemples frappants Ctant la maitrise par les eleves des outils symboliques et I’appel au support social. Nous examinons une etude que porte sur l’impact de ces deux variables majeures sur l’apprentissage. On presente aussi des exemples de recherches oh la technologie sert de rCvClateur en mettant en Cvidence des modalites d’apprentissage qui sont difficiles a observer dans la classe.

Educational reform efforts in the US and other countries come to naught, we argue, because ideas that are at the heart of the reform often are not adequately developed. Researchers tend to overestimate the heuristic value of their ideas. Recent breakthroughs in technology offer a solution to this enduring problem. Technology provides a means of systematically testing aspects of the learning process that otherwise might get lost in a sea of detail, two prominent examples being students’ mastery of symbolic tools and their utilisation of social supports. We discuss work that examines how these two key variables affect student subject-matter learning. We also present examples of research where technology serves as an enabler, making more visible certain kinds of learning that are difficult to observe in the classroom.

INTRODUCTION As m a n y in t h e international education psychology field realise, t h e US has been reforming its schools f o r o v e r a decade now. T h i s is not an entirely new happening on t h e A m e r i c a n scene. One historian argues t h a t educational Requests for reprints should be sent to Dr. Richard S. Prawat, Michigan State University, College of Education, Erickson Hall, East Lansing, Michigan 48824, USA.

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innovation and reform in the US waxes and wanes on a 30 year cycle: The 1930s, 1960s, and 1990srepresent a more liberal, or progressive, swing of the educational pendulum; the 1920s, 1950s,and 1980s can be characterised as a time when teachers were strongly encouraged to return to what is euphemistically referred to as “the basics” (Schlesinger, 1986).In two earlier reforms, the shift in educational attitude towards a more progressive or ambitious pedagogy was justified by citing new research and theory in psychology and philosophy. The 1930s reform was based largely on Dewey’s ideas about teaching and learning-or, at least, on US educators’ interpretations of Dewey’s ideas about these important matters. Dewey’s views, reform advocates argued at the time, were thought to support a more child-centred and activity-oriented type of instruction, especially at the elementary school level. Following the seemingly inevitable conservative counter-reaction, the progressive ideology in education reasserted itself in the 1960s in the US. Dewey was no longer the chief scientific spokesman for reformers; Jean Piaget had assumed this role. However, as Silberman (1970) made clear in his book, an influential critique of traditional education at that time, Piaget was thought to share with Dewey a key tenet of the reform agenda: Namely, the notion that, to quote Silberman (1970, p.215), the child “learns through doing”. In the 1990s reform, very similar rhetoric on learning and teaching is again surfacing, albeit under the rubric of what is now termed “constructivist” theory. Cuban (1990, p.4), among other commentators, comments on the cycle of reform and reaction that characterises US education: Within the present moment of reform, another generation of reformers is fighting against a technical subject-centered form of instruction expressed in mastery learning, measurement-driven curricula, and bookkeeperlike accountability. Those researchers and practitioners who herald cooperative learning, active student involvement and the virtues of desktop computers that interact with students bring new meaning to Yogi Berra’s observation [i.e. “It’s dejja vu all over again”].

The issue facing educational psychologists in this country is whether or not, in fact, Cuban is right about the cyclical nature of reform. What are the chances that the current round of educational reform will go the way of reform efforts in the past? The jury is still out on this issue, we argue. The future of educational reform in the US, an effort that has preoccupied educational psychologistsfor a number of years now, appears to depend on a number of factors, one of the most important being the issue of how new ideas about learning and teaching are framed or presented. Too often in the past, researchers or theoreticians have over-estimated the heuristic value of their ideas, leaving it up to others to work out the messy details, often with

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disappointing results. A couple of examples from previous reform efforts illustrate the depth of the problem. Dewey, for one, was shocked to see what sense many well-meaning educators made of his ideas about education. Many would-be reformers, Dewey lamented towards the end of the 1930s reform era, mistakenly think that student self-direction is the only antidote to the sterile presentation of content. Teachers in many progressive schools, he concluded, act as if they believe that the “orderly organization of subject matter is hostile to the needs of students” (in Cremin, 1962, p.234). “What is needed in the new direction,” he wrote (1940, p.294), revealing how little people really grasped his ideas, “is more attention, not less, to subject matter” [emphasis added]. Dewey was responsible for his lack of clarity to a great extent. When he did write about education, he was often clearer about what he did not like than what he was proposing as a concrete alternative (cf. Tanner, 1991). Would-be reformers who based their thinking on Piaget’s theory in the 1960s experienced similar difficulty in moving his notions from research to practice. Piaget, like Dewey, was concerned mainly with epistemological issues relating to knowledge development. Given the ambitious scope of this agenda, he devoted little time to spelling out the practical details of his theory. As a result, many educators relied on would-be interpreters of Piagetian theory, and thus were exposed to his ideas in a third-hand and superficial way. What many educational reformers picked up on was the most unusual and counter-intuitive aspect of Piagetian theory: the “ages and stages” notion. “Ages and stages”, applied to the teacher’s own instructional situation, misrepresents the richness and generativity of Piagetian theory, resulting in a number of questionable practices on the part of teachers-not intervening in mathematics due to the fact that 5-year-olds “lack the number concept” being but one example. What was missing in the 1960s was the depth of understanding necessary to connect Piagetian theory to the actual phenomenon the theory is attempting to explain (e.g. the role logicalmathematical structures play in students’ mathematical learning). There is a very real possibiliy that mistakes made during past reforms will recur, this time under the guise of what is termed a “constructivist” approach to learning. Like the active learning guidelines extracted from Dewey’s abstract views about the nature of knowledge, the current constructivist admonition embraced by many teachers-“pay attention to the students’ own efforts after meaning”-leaves much to be desired from a practical standpoint. Arcavi and Schoenfeld (1992, p.333) capture the quality of this advice well when they tell teachers to search for “the germs of genuine knowledge in what students say” and think about “iflhow those ideas can be exploited”. The problem with staying at this level of abstraction is that teachers, in their eagerness to make use of the new theory, are likely to end

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up proceduralising the theory. Dewey and Piaget’s complex views were often converted to simple rules (e.g. “Provide hands-on experience). A similar thing may happen to constructivism, with predictable results.

GOOD IDEAS, CONSTRUCTIVIST OR OTHERWISE, NEED TO BE SPELLED OUT What the history of educational reform in the U S suggests is that it is dangerous to assume that good ideas will “sell themselves”. Dewey himself warned educators against making this assumption, which he regarded as yet another instance of a common problem-the “great vice” of intellectualism (192511981). Ideas ought not be separated from concrete reality, Dewey argued. When this happens, they become mere verbalisations, removed from the objects and events they were meant to explain. “When intellectual experience and its material are taken to be primary,” Dewey insisted, “the cord that binds experience and nature is cut” (192511981, p.29). Educational reform in the U S has been hampered by the failure to heed Dewey’s warning. This is especially true in the area of learning theory. Seldom have the necessary steps been taken to adequately connect new ideas about learning to the reality of children attempting to come to terms with subject-matter curricula in the public schools. How do educational psychologists, in the context of the current reform, guard against the misappropriation of abstract ideas in the learning domain? This is a difficult question. We think that the skilful use of technology offers one possible solution to this problem. Technology allows educational psychologists to manipulate each of the main sets of resources-symbols, physical artifacts, and social support-that constructivists believe enter into any complex learning environment. We will provide some examples of the treatment of each of the important factors in a technological setting or environment. We realise that, in all cases, the researchers designing computer-enhanced learning environments have not €ocused exclusively-or perhaps even strongly-on any one of these factors in their research; we also realise that learning always involves a complex interaction among all three of these variables. Nevertheless, we do believe that technology, as a site for studying learning, affords researchers a unique opportunity to study the effects of each of these multifaceted elements. We also believe that the failure to capitalise on this opportunity will have deleterious effects on the future of social constructivist theory in particular and on the educational reform movement in the U S in general. Before developing this notion, further, however, more needs to be said about constructivist learning theory. At the core of this theory is the notion that learners, as members of “discourse communities,” are active constructors of their own knowledge and understanding (Prawat, 1995,


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1997). Very little that individuals hear or read is taken at face value, according to this view. Of necessity, all new learning is filtered through the lens of prior knowledge. Some constructivists argue that this prior knowledge is best conceived as a set of schemas or frames that one uses to impose structure on new input. Others, increasingly, are looking outward towards the social and artifactual environment. This second view, more consistent with the postrnodern emphasis on alternative, non-rationalist or non-realist epistemology (Rorty, 1979), owes much to earlier theorists like Vygotsky. The individual draws on three main resources in constructing knowledge, according to the Vygotskyian view that has gained currency among educational psychologists in the last three years: symbolic or “psychological” tools, physical or technical tools, and social supports or scaffolds of various sorts. All three of these learning resources play a prominent role in sociocultural theory, as suggested. Vygotsky (1978, p.55) was quite explicit in drawing distinctions between the first two: The [technical] tool’s function is to serve as the conductor of human influence on the object of activity; it is externally oriented; it must lead to changes in objects. It is a means by which human external activity is aimed at mastering, and triumphing over, nature. The sign [psychological tool], on the other hand. changes nothing in the object of a psychological operation. It is a means of internal activity aimed at mastering oneself; the sign is internally oriented. These activities are so different from each other that the nature of the means they use cannot be the same in both cases.

Vygotsky viewed both symbolic and physical artifacts as sources of change and development for the individual. Both serve as conduits for what Wertsch et al. characterise as the importation of “foreign structures and processes” into individual mental functioning (1993, p.352). Some of Vygotsky’s psychological descendants, termed “activity theorists”, assign primary importance to objects or physical artifacts as prime mediators of learning; others, like Wertsch (1985) and Cole (1996), assign primary importance to signs and symbols. Both agree, however, on the overarching importance of the social dimension in learning. The importance of the social dimension was a notion, unlike disagreements about the centrality of technical or psychological tools, about which Vygotsky was unequivocal: It is primary in time and in fact, he argued, “The individual dimension of consciousness is derivative and secondary” (in Kozulin, 1990, p.82). Social constructivists have made some progress towards spelling out the details of their view of learning. Despite this fact, much work remains. The danger, and this is the main idea we wish to highlight in this review, is that this work will not be done, that social constructivist models of learning are too complex to be adequately studied in the classroom environment. If so, there is a real danger that this view of learning will go the way of previous

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complex views of learning: proceduralisation, followed by an intense counter-reaction of the sort experienced in the 1940s and 1970sby advocates of Deweyan and Piagetian views of teaching and learning.’ Proceduralisation, we argue, stems from a failure to come to terms with what might be termed the “mid-level’’complexity of a theory. One ought not to assume that a constructivist view of learning is any more accessible than those that preceded it. Lave and Wenger (1991) provide a case in point: They worry that much of the richness of their notion of situated learning, based on Vygotskyian theory as outlined earlier, is being lost on reform-minded educators-who view this complex attribute (i.e. situatedness) as just another quality of worthwhile instructional activity. Situatedness implies far more than many reformers assume, Lave and Wenger argue. It forms the basis for claims about the relational nature of knowledge, the notion that “agent, activity, and the world mutually constitute each other” (p.33).

TECHNOLOGY AS A SITE FOR STUDYING LEARN1NG Despite the gloomy forecast, many social constructivists have discovered the merits of using specially crafted learning environments to study learning. This involves more than the use of technology as a way to enhance student learning; much research and development work has already demonstrated its power in this regard. It involves, rather, the use of technology as a way to uncover or unpack the learning process itself. The power of technology as a site for studying learning is becoming increasingly apparent in the mathematics, science, and literacy domains. In mathematics, for example, technology brings to the fore important links between student actions and perceptions, on the one hand, and various formal mathematical

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A good example of backlash generated by the oversimplified application of a complex idea is found in a recent “op ed” piece in the New York Times. In this piece, Lynne Cheny (1997, August 11) singles out for criticism what she explicitly terms the “constructivist” approach to mathematics. It is especially ironic, Cheney argues, that this approach-which she pejoratively labels “fuzzy math”-is becoming a force in mathematics: “In a field distinguished by reliance on proof,” she writes, “an unproven approach is being taken in thousands of schools” (p.13). Thomas Romberg, a distinguished mathematics educator, replies to Cheney, reinforcing some of the concerns raised here: sometimes teachers put too much emphasis on general ideas, he concedes. As a result, he argues, they may miss the point: teaching problem solving without having students learn algebraic procedures is cited as one example. The problem Romberg discusses, we submit, is indicative of a failure to adequately develop new ideas in the learning domain.


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representations, on the other. Using social constructivist language, technology allows the researcher to study the complex interactive process of symbolic mediation, tool use, and social support in mathematics in ways that go well beyond what would be possible in a more naturalistic environment. A good example of the use of technology as a site for studying mathematics learning is a recent study by Thompson and Thompson (1994). As is typical in research on learning involving the interaction between “technical” and “psychological” (i.e. symbolic) tools, the first phase of the study involves introducing individuals to the tool. In this case, Thompson and Thompson presented both teachers and students with a specially designed computer program that allowed them to manipulate the speed of two objects-a turtle and a rabbit-while simultaneously recording elapsed time and distance travelled. The apparatus was constructed to highlight two aspects of the idea of speed as a rate: first, the notion that one can measure the total distance an object (a rabbit or turtle) travels in units of speed-length (e.g. 20 feet per second); second, that travelling a distance at some constant speed (e.g. 100 feet at 20 feet per second) will produce an amount of time (e.g. 5 seconds). The hope was that, as individuals interacted with the program, plugging in different assigned values on over-and-back trips made by the animals in response to specific queries, they would see the merit of reasoning multiplicatively about completed motion, viewing it as corresponding segmentations of accumulated distance and accumulated time. In other words, they would understand the important idea that the distance an object travels and the amount of time it needs to travel that distance are in direct proportional relationship. Thompson and Thompson (1994) report a rich case study of one sixth-grade student’s efforts to master this challenging concept during two sessions spent in interaction with both machine and teacher. Researchers in this “teaching experiment” focused most heavily on the quality of the teacher and student interaction. What is interesting is the way the technological tool recedes into the background, allowing issues of individual learning and teacher social support to come to the fore. Without a common referent (i.e. values derived from manipulating the speed of the animals), the ways in which student and teacher speak past each other would not have been evident. Specifically, Thompson and Thompson demonstrate how the student defined a task (i.e. finding a speed that would allow the rabbit to go back and forth in seven seconds), that was at variance with how the teacher was thinking about it, which was to equate partitions of time with partitions of distance. The important point to keep in mind is that technology, in this case and in many others, provides a unique site for exploring complex issues relating to student learning.

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TECHNOLOGY AS A SITE FOR EXAMINING SYMBOLIC MEDIATION We begin with this aspect of the learning situation not because it is the most developed area of research in a technological sense but because it may be the most important. Clearly, symbols in the form of rich visual images, words, ideas, or concepts are central to the knowledge-building process. One does not have to look far to find strong support for this hypothesis. Powerful ideas developed within the disciplines, like point of view in literary studies or the notion of additive composition in mathematics have enormous heuristic value (cf. Prawat, 1993).They are, to quote Bruner (1969, p.121), “lithe and beautiful and immensely generative”. Prawat (1997) traces ideas back to their perceptual and imagistic roots, citing for support a growing body of research in the history of science that involves culling through the rough notes of great thinkers like Einstein and Darwin. This work (Ghiselin, 1969;Miller, 1987) sheds considerable light on the process of idea construction, which relies heavily on visual imagery and metaphoric thinking. Einstein reported using a number of elaborate and imaginal “thought experiments” in coming up with his startling new ideas. Darwin also reportedly hit on one novel notion-that of nature selecting and rejecting-by reasoning analogously from what was a familiar realm of experience for Shropshire countrymen, animal breeding, to a much more distal domain, that of the fossil record. We know very little at this time about the origin or genesis of ideas as symbolic mediators-or even the exact nature of the role they play in the learning process. One theory is that they develop through a process of metaphoric projection. Johnson (1987) and Prawat (1997), among others, have argued for this view. Johnson believes that ideas have their basis in physical experience. An abstract concept like symmetry in mathematics, the notion that zero is a point of demarcation separating positive and negative numbers on an imaginary number line, for example, has its origins in the physical realisation that our bodies are symmetrical. Johnson, however, assumes a traditional perspective in arguing that metaphoric meaning serves propositional meaning: it provides a way to draw more basic or primitive types of understandings into language, which constitutes the primary way of knowing. Much of what is termed propositional understanding, according to Johnson, is rooted in nonpropositional, physical experience. Others, like Boyd (1979), take a more radical stance. Instead of viewing metaphor as a way to accommodate knowledge to language, Boyd sees it as a mechanism for accommodating language to the world. He argues that metaphor moves ahead of language and pulls it forward rather than the other way around, as Johnson suggests. It is the creative insight provided by metaphor, a process that occurs outside language, that leads to the adoption of new language. Citing examples from science, Boyd (1979, p.404) declares:


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“In each case, the improvement in linguistic usage resulted from the discoveries about the world, rather than from attention to linguistic rules or conventions.” The fact that the relationship between language and meaning has become murky recently as a result of these and other disagreements has heightened interest in the process of symbolic mediation (cf. Gibbs, 1994). As the foregoing discussion suggests, there is a great deal more we need to know about how ideas and other symbols function in mediating human understanding. This is one of the most exciting areas of current and future technology-situated learning research. One of the best examples of this genre of work is being done on the west coast of the US by a scholar named Jeremy Roschelle. Roschelle (1992) developed and was able to exploit a unique kind of software in a series of studies aimed at fostering what he terms “convergent conceptual change” in students. The process he developed to facilitate this type of learning has students interacting directly with two ways of representing the same phenomenon. Roschelle uses a split-screen visual display to represent, on the right-hand side of the screen, an object as it might appear falling through space. O n the left-hand side of the screen that same phenomenon is represented in a Newtonian way, using a particle and arrows that signify velocity and acceleration. The former, not surprisingly, is labelled the “observable world”, the latter, the “Newtonian world”. The task of students, assigned in pairs, is to figure out how to map the Newtonian representation onto the real-world representation in such a way as to produce the effect that most students anticipate when they view objects hurtling through space: a downward, slightly parabolic free fall. Students are not told that the arrows on the right-hand side of the screen represent velocity and acceleration; instead, they are simply labelled “thin” and “thick” arrows respectively. Like Thompson and Thompson, Roschelle presents detailed qualitative data describing the process two students-Carol and Dana-go through as they slowly work their way towards a more mature scientific understanding of accelerated motion, the idea represented on the computer screen. An understanding of this complex idea, defined scientifically as the notion “that acceleration is the derivative of velocity with respect to time”, was thought by Roschelle to be tantamount to the realisation that the thin and thick vector arrows must operate at right angles to one another to replicate the downward, parabolic action of the real-world object. Headway in this regard was made by one of the girls 15 minutes into the second session, when she exclaimed, “It pulls it,” referring to the thicker acceleration arrow and gesturing towards her body with her hand to support her contention. Roschelle reaches the following conclusion about the understanding students were able to create in the technological “work space” he provided for them: although students did not necessarily master the language associated with the Newtonian concept of acceleration, they did acquire

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rudimentary or core understandings. Thus, they seemed to penetrate to the heart of the matter as evidenced by the metaphors they developed (i.e. “pulling” and “hinging”) to explain the dynamic nature of velocity change and the way it is constrained by the vector addition triangle (Roschelle, 1992, pp.262-263). (A second student, for example, explained his grasp of this difficult concept by saying, succinctly, “See, it [acceleration] pulls it [velocity] down”.) One way to think about what Roschelle is trying to accomplish through the use of technology is that he is to provide students with an interactive medium that affords them the opportunity to construct the “physics lenses” necessary for viewing real-world phenomena in a more scientifically sophisticated way. His envisioning machine makes this mapping explicit. Students are trying to understand the Newtonian representation of velocity and acceleration (on the right-hand side of the screen) so that they can eventually use it as a lens for viewing real-world examples of objects falling through space. To facilitate the lens-crafting and lens-fitting process, the real world is itself presented in a highly schematised form (on the left-hand side of the screen). Patrick Thompson, in an interesting recent chapter (1995), sees great potential in the use of technology as a research site for studying the process of symbolic mediation. He acknowledges that in his work with individual students (see earlier), he, as a researcher, functions like a teacher in the classroom in helping to “co-create” the very phenomena he is trying to study. He cites (1995, p.127) a specific instance, his work with a girl, JJ: My relationship to this girl, JJ, was that I brought her to the teaching experiment, designed special software that would support discussions about speed and rate, asked questions of her about situations surrounding the software, encouraged her to abandon her (unthinkingly) self-imposed constraint that she calculate all intermediate results, and prepared for the next lesson by thinking about her understandings as expressed in response to my questions.

Thompson sees this work as social constructivist in spirit even though, in a figure-ground kind of relationship, he prefers to focus on the individual and her construal of the situation rather than on the social relationship per se. Thompson helps allay the fear that many social constructivist researchers feel when asked to comment on research in technology-rich environments like those discussed in this paper: namely that the researcher has created a situation that is so artificial in his or her attempts to study symbolic mediation in complex knowledge domains that the results are apt to have very little applicability to the process of education as it is currently defined. Thompson reminds us that even in very ‘‘controlled’’ technology environments complex social processes occur and can be studied. There is a second argument against the “artificiality” notion that ought to be considered. The heart of this argument is implicit in much of what has


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been said to this point about the use of technology to study the process of symbolic mediation and social support. It is based on two premises: first, thanks to work being done by scholars like Jean Lave (1988) and Barbara Rogoff (1990), we can no longer consider the classroom the prototype for effective learning. School culture, in other words, ought not to be considered the benchmark for evaluating learning environments, a practice that in the past has led educators to view non-school environments as somehow deficient or degraded (Prawat, 1992). Lave’s work (1988) on everyday problem solving turns this argument on its head: individuals can be extraordinarily good problem-solvers when they can draw on the environmental supports evident in most examples of non-school contextualised or situated learning. Regarding the classroom as but one of many learning environments frees researchers up to look outside the school environment for examples of rich, situated learning. This second premise directs their gaze towards the academy-specifically towards academic discourse communities. Historically, the academic disciplines have done a good job of turning up powerful new ideas, like the “greenhouse effect”, which become the symbolic tools that open up aspects of the world that otherwise would remain hidden from sight. For this reason alone, the use of technology to simulate the generative learning that goes on within disciplinary learning communities seems justified. This argument applies to more than the process of symbolic mediation, however, which has already been discussed. It also applies to the process of social support in learning. The power of technology in shedding light on this crucial process, we argue in the next section, lies in its ability to present an array of social possibilities that are rarely, if ever, observed in the classroom learning environment. One of the strengths of networking technology, which gets at the role of social support, is that it allows the researcher to systematically vary the amount of support available to students along several dimensions, including the degree of expertise being provided. Possibilities for networking range from connecting students to novice peers, who share an interest in the topic but who may add very little in the way of new information, all the way to top experts in the field who can bring cutting-edge knowledge to bear in advising their charges on research projects and papers. Researchers can also look at how networking relationships change and develop over time, as reflected in the quality and quantity of shared interaction. They can look at how teacher involvement affects the process, what sorts of overtures from novices are most likely to result in positive responses from experts, and so forth, systematically examining a number of possibilities. One advantage in the use of technology as a way to study social variables in learning, as Scardamalia and Bereiter (1992) point out, is that an electronic record can be kept of all of transactions. Contributions can be

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preserved over time, allowing researchers to study discourse events that are relatively rare in the normal classroom-students asking “wonderment” (i.e. authentic) questions, for example, which are actually taken up and responded to in thoughtful ways by adults. Insights gained from studying positive and negative examples of social support in networking situations should prove highly useful to teachers intent on improving the quality of the social support they provide to students in their classrooms.

TECHNOLOGY AS A SITE FOR EXAMINING THE ROLE OF SOCIAL SUPPORT Scardamalia and Bereiter, two prominent researchers studying the role of social support in a technology-mediated environment, focus on what they term “progressive inquiry”. This is an unusual kind of classroom learning in that it is driven by the students’ genuine need to know rather than, in the case of “text-based’’questioning, by their need to reach closure on a project. Such learning is typical in disciplinary communities, Scardamalia and Bereiter (1992, p.196) argue, thanks in large part to the type of social networks found in such settings: Progressive inquiry is sustained in the sciences and scholarly disciplines, often with very little in the way of formal procedures. What seems to be required is a community in which the advancement of collective knowledge is highly valued and individual contributions have the possibility of being immortalized. Schooling, in its several common varieties, lacks these characteristics, and so it should not be surprising that the pursuit of knowledge is either reduced to a routine task or is left to individual initiative.

Scardamalia and Bereiter hope that the type of technology they have constructed around such projects, which allows students who are working on a project to network with students in other classes and schools who share an interest in the project, will approximate the kinds of rich social supports available to those operating within the scholarly disciplines. Scardamalia and Bereiter (1992) had certain specific features in mind as they designed their system which approximate the kinds of constraints and resources available to those operating from a disciplinary base. In a disciplinary community, they argue, the development of knowledge is an endeavour that all (or nearly all) contribute to and benefit from. They thus took care to ensure that student participation was widespread. (This, in fact, was later cited as a plus for their approach; in most classroom projects, a subset of students take the lead.) This is not to say that student contributions to the database were blindly accepted, however. In disciplinary communities, advances to knowledge are subject to a rigorous system of peer review, a feature also taken into account-although in a more relaxed


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way-in the design of Scardamalia and Bereiter’s networking system, CSILE (Computer-Supported Intentional Learning Environment). CSILE is a classroom database consisting wholly of student-generated “notes” on topics relevant to the teacher’s curriculum. It is accessed via networked microcomputers, typically eight per class. Rather than appropriating information developed by others, students create their own base of knowledge from the bottom up. They comment on each other’s notes, pose and answer questions, introduce new topics, and otherwise make more visible the processes by which they contribute to and use the body of knowledge that they are developing within a carefully fashioned context that supports peer interaction. All contributions to the database are accessible to participating students and teachers (Scardamalia & Bereiter, 1993/1994, p.277): The community data base serves as an objectification of a group’s advancing knowledge, much as d o the accumulating issues of a scholarly journal but with additional facilities for reframing ideas and placing them in new contexts . . . CSILE is designed to frame students’ ideas in ways extensible to the broader knowledge-building community and, concomitantly, to resist discourse frameworks workable only in schools.

It is thought that exposure to the ideas and work of fellow students will instil in students an appreciation for the collaborative nature of scholarly work. More to the point, the networking approach developed by Scardamalia and Bereiter enables researchers and others to take a closer look at the actual processes by which students develop into a community, and the particular aspects of this process that are useful in helping students to learn. The accumulation of student notes allows researchers and teachers to track student discussions and individual contributions to the database. Using such information, researchers and teachers can manipulate variables to maximise the positive effects of the technology. For instance, Scardamalia and Bereiter (1992, p.195) found that “an alternation between live discussions and individual work at computers serves to capture the motivational and cognitive advantages of both media”. This finding, in turn, raises a host of additional questions. Using the database provided by CSILE, researchers are in a position to more closely identify the exact mix of discussion and individual work that will optimise the learning process for a particular group of students. Scardamalia and Bereiter (1993/1994) argue that they are focusing on the “process” aspects of expertise in carefully networking students with other students. A key feature of this process is the “intentional” nature of the learning being evoked. What this translates into is that students are “trying to achieve a cognitive objective-as distinct from simply trying to do well on school tasks or activities” (p.266). Implicit in this work is the possibility that

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students are not just becoming more efficientor energetic in their pursuit of classroom learning. One has to allow for the possibility, yet to be examined, that participation in CSILE leads to qualitative as opposed to merely quantitative changes in learner behaviour. Thus, the fact that students are required, under such a system, to assume greater control over and responsibility for their own learning may lead to lasting changes in how they view themselves as learners. A second line of work discussed in this section also focuses on process, but with an important difference. In this approach to networking, developed by O’Neill, Wagner, and others at Northwestern University, students participating in science research projects are systematically connected up with experts in the field who are willing to serve as long-distance mentors (“telementors”, in project parlance). The attempt here, as with the previous project, is to promote a more authentic type of scientific inquiry. Capitalising on an idea that is relatively new on the educational scene (cf. Ruopp, Gal, Drayton, & Pfister, 1993), O’Neill and Gomez (1994), and O’Neill, Wagner, and Gomez (1997) have figured out a way to link high-school students with “telementors”, science experts who communicate with their mentees using electronic mail (Email). The expert mentors serve as a valuable resource for students, much as peer experts do for scholars operating within a disciplinary community; they direct students to unique data sources and provide them with thoughtful feedback on their research questions and data-analysis strategies. The goal of the telementoring project is to “develop an audience of scientists who can offer students advice and criticism on an ongoing basis” (O’Neill, Wagner, & Gomez, 1996, p.39). These experts contact high-school students using the students’ assigned personal Email accounts. At present, two collaborating high schools in suburban Chicago, a science museum in San Francisco, and an atmospheric science research laboratory at the University of Illinois at Urbana-Champaign are networked; additionally, a specially designed piece of software-the Collaboratory Notebookallows students and experts across these sites and over the internet to share text and other types of information. The Collaboratory Notebook automatically creates links to new entries in the notebook written in response to previous entries. Entries from students are structured according to research strategy (i.e. question, conjecture, plan), which allows the expert mentors to follow the development of student thinking as they move through their projects. Experts willing to mentor students in their project work were recruited for participation in the telementoring project through Usenet and scientific listservs. Potential mentors were told about the project and informed that


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they would be asked to assist students in their explorations, “not by giving answers and spending lots of time explaining basics, but by asking questions about the research, the data collection, and/or the methods” (O’Neill et al., 1997, p.4). Mentors were recruited to match student research interests, with one volunteer assigned to each group of students. Subsequent analysis of mentoring data, including student judgements of success and failure, and more objective measures reflecting the volume of mentor-mentee Email traffic, reveals some interesting patterns: ratings of success and volume of traffic, for example, were not correlated. Student perceptions of factors like the amount of respect received from the expert-but not measures of mentor action like the number of mentoring functions evidenced over time4orrelated with student ratings of success. Additional analyses, especially those that track perceived changes in social support over time, should provide grist for researchers interested in the development of expert mentoring relationships in learning. As with the use of technology to create sites where the process of symbolic mediation can be studied, which was reported on in the first section, or where students can be networked with other students (this section), O’Neill and his colleagues have created an environment that is richer and more amenable to study than that existing in the traditional school environment. In the traditional school setting, expert mentoring for students over the long term (i.e. across several projects) is difficult if not impossible to arrange. Professionals who excel in a given field may make spot appearances in school to talk and answer questions about their work. In research communities, however, long-term sustained expert mentoring is commonplace. Providing high-school students with similar sustained expert mentoring allows learning researchers to test the hypothesis that such support is a necessary-if not sufficient-condition for students to achieve levels of understanding that approximate the kind evidenced by scholars in the disciplines. One final use of technology as a site for research needs to be mentioned in this review: this is the use of technology as an “enabler” in the learning process. Here technology plays a role that is harder to describe than that of serving as a site for examining symbolic mediation or studying the role of social support in the learning process. Technology that functions as an enabler allows certain kinds of learning to come to the fore that otherwise might remain hidden, given memory or data-management demands. It amplifies cognitive processes. It may, to some extent, transform those processes as well. This second stronger claim deserves attention. It has been advanced by Roy Pea (1985), among others. We will briefly examine the use of technology as an enabler, and possibly a transformer, in the next section of the paper.

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TECHNOLOGY AS AN ENABLER (OR TRANSFORMER) IN THE LEARNING PROCESS There are a number of examples that illustrate the use of technology as a means of making more visible certain kinds of learning that otherwise might not be evidenced in a learning environment. One of the most widely known uses of “enabling” technology is that described by Rand Spiro and his colleagues at the University of Illinois (Spiro & Jehng, 1990; Spiro, Feltovich, Jacobson, & Coulson, 1995). This is the instructional hypertext environment designed by Spiro that allows students to flexibly access annotated segments of the movie Citizen Kane. Thus, students are able to do in minutes what it otherwise would take them hours to achieve-quickly scan examples of Welles’ portrayal of themes like “wealth corrupts”, which are liberally sprinkled throughout the film. In this way, KANE, an acronym for the hypertext learning environment Spiro et al. developed as a prototype, allows certain kinds of integrative thinking to emerge in the course of interpreting a literary film that otherwise might remain hidden or inaccessible given memory load and physical dexterity requirements (i.e. stopping and starting a film projector). The hypertext design is well suited to a certain kind of difficult subject matter-that characterised as “ill-structured’’ by Spiro and his colleagues. 111-structuredknowledge domains are said to be those that lend themselves to multiple interpretations: those domains, like medicine, history, or literary studies, where, according to Spiro et al. (1995, p.102), any one “overly limited version of what is ‘correct’ will miss too much of the complexity that must be mastered for sufficiency of rich conceptual understanding and fullness of case coverage.” KANE is thought to be an excellent mechanism for examining instances of what are, at best, loosely defined instances of general or fairly abstract concepts. Not only does KANE allow students to visit and revisit prime examples of certain literary conceptual themes in the movie, it also provides additional information about each theme in the form of expert commentary. Embedded in the commentaries are pointed instructions about what to look for in deciding if a particular concept applies to a particular segment or excerpt. The role that a technology like KANE plays in enabling certain kinds of student understanding to come to the fore is made explicit by Spiro and his colleagues (1995, p.97): Rather than relying on sporadic encounters with real cases that instantiate different uses of a concept, the learner sees a range of conceptual applications close together so conceptual variability can easily be examined. Learning a complex concept from erratic exposures to complex instances with long periods of time separating each encounter, as in natural learning from experience, is not efficient.


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Spiro’s use of hypertext may be the most prominent example of enabling technology, but one other powerful use of this technology should be mentioned: this is the scientific visualisation software being used in many high-school science classrooms as a mechanism for graphically displaying complex arrays of data. Like KANE, this use of this technology allows students to handle complexity that otherwise might prove debilitating. One prominent example is Climatewatcher, developed by Gordin and Pea at Northwestern University (1995, 1996). ClimateWatcher allows the science educator to distil enormous amounts of data for students and “re-present” them holistically using a range of colours. Thus, the amount of solar energy arriving in the earth’s atmosphere at two points in time (i.e. January and July), determined by processing a large amount of temperature data gathered at different points around the world, can be represented as bands of colour, red-orange standing for the hottest, blue for the coolest. The same scientific visualisation technique can be used to represent the surface temperature of the earth at the same two points in time (January and June). This allows students to detect patterns and relationships in the data that otherwise might be lost amid the jumble of numbers. One interesting anomaly, for example, is apparent when one compares the solar energy and earth surface temperature data for the northern and southern hemispheres: less sunlight is received in the northern hemisphere in July than in the southern hemisphere in January, yet the surface of the earth is warmer in the north than in the south. This interesting phenomenon, which students can now see and discuss, is accounted for by two facts: it takes less energy to raise the temperature of land than water, and the land mass is greater in the northern hemisphere. The important point to keep in mind in this work on scientific visualisation (SciV) is that the use of software like Climatewatcher allows learning researchers to study aspects of relational thinking in science that otherwise they would not be able to do. As Gordin and Pea explain (1995, p.261): “SciV’s can provide a more accessible inscriptional system for students to understand the subject matter, processes, and results of science.” SciV thus belongs in the arsenal of the educational psychologist intent on using technology to delve into details of the social constructivist learning process in ways that were unimaginable in previous eras. As indicated, Roy Pea, a prominent technologist, makes a stronger case for the role of technologies like hypertext in learning-arguing that they not only amplify mental functioning, they reorganise it. Thus, addressing the role of technology in pushing the problem solving process, Pea writes (1985, p.175): The quantifiable products of problem solving have indeed been enhanced, as the amplification metaphor would lead us to observe, but the software has also restructured the thinking activities involved in such a major way that computer

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users come to discover new methods of thinking about their mental tasks and unanticipated ways of using technologies. After reviewing the literature supportive of the amplification hypothesis, Pea cites others, like Bruner, who maintain that the “order of magnitude” changes associated with cognitive tools like the computer can lead to a qualitative restructuring of the functional system for thinking. The examples that Pea offers, however, appear, even by his reckoning, to represent more instances of where mental functioning is reoriented rather than restructured. One example Pea cites is the use of electronic spreadsheets in budgeting. This cognitive tool allows financial planners to test various budgeting scenarios instantaneously, allowing for the application of “what-if” thinking in a way that hitherto was difficult if not impossible when working with ledgers. Electronic spreadsheets, therefore, have allowed budgeteers to import a kind of thinking into a task that could not be evidenced much prior to the development of the new technology. Less clear is whether or not this represents an example of a qualitative change in “what-if” thinking on the part of individuals-or simply an example of an increase in the opportunity to employ that kind of thinking on the job. The second example discussed by Pea, the use of problem-solving software in mathematics, is equally ambiguous. AlgebruLand, a popular instance of this genre of tool, follows the norm in performing the “tactical” moves needed to solve certain equations. Dispensing with these functions (i.e. arithmetic calculations and algebraic operations) supposedly frees students to focus on more conceptual aspects of the equation-solving process, like testing the effects of various operators in the search for a solution path. The present authors, however, unlike Pea, are more inclined to treat these and other examples of cognitive enhancement as instances of amplification rather than reorganisation. This is not to say that we discount the claims made by researchers like Pea. It may be that processing efficiencies associated with the use of technology can lead to qualitative improvements in individuals’ mental functioning. Siegler and Jenkins (1989) present data on the acquisition of mathematical counting-on strategies on the part of preschoolers, which support the contention that efficient use is associated with improvement in use. The latter, these researchers were surprised to note, occurred most often when students were working on easy problems-those that had been solved correctly at earlier points in the experiment-rather than on more challenging problems, as many theorists assume. These results suggest that proficient practice, rather than perturbation or conflict, sets the stage for creative experimentation. Although the Siegler and Jenkins study did not involve the use of technology, it is consistent with the notion that cognitive amplification, under certain conditions, can result in cognitive


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reorganisation. This whole issue, obviously, deserves much closer scrutiny than it has hitherto received.

SUMMARY We began this review of promising developments in educational psychology by highlighting a dilemma faced by social constructivists who are playing a leadership role in the current educational reform movement. This dilemma actually dates back to earlier periods of reform, however. It results from the fact that educational reformers often seize on new ideas-in the current case, constructivist views of learning-long before researchers have had the opportunity to work out the concrete details. Dewey and Piaget both experienced considerable chagrin over what many well-meaning educators had done with their complex notions about teaching and learning. There is a danger of that happening once again in the US. The “proceduralisation” of ideas in previous reforms invited an inevitable counter-reaction from educational conservatives, spelling doom for the reform effort. One positive sign that mitigates against the bleak scenario just described, we argue, is the creative way researchers in the US are making use of technology to study complex aspects of the new learning theory. We described three such research programmes: the use of technology to better understand the process of symbolic mediation, the use of technology as a site to examine the process of social support in learning, and the use of technology as an “enabler” to surface certain kinds of complex learning that otherwise would remain hidden-buried beneath the mass of detail that learners would need to master to operate at that level. We hope that our way of presenting these programmes of research will spur interest in the important theoretical and practical work yet to be done.

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