The conceptual change approach has emerged from an effort to provide answers to questions regarding the re-organization of conceptual knowledge. Over the years, criticisms coming from socio-cultural perspectives of learning, as well as from researchers interested in other factors influencing learning, such as motivation and personal epistemologies, has brought forward aspects of learning that have been neglected in initial accounts of conceptual change.

This symposium aims at reframing the conceptual change approach in learning and instruction, in light of recent theoretical considerations and empirical evidence .

The contributors take different perspectives on the issue of conceptual change.

Ola Halldén, Åsa Larsson, and Liza Haglund explore different models and metaphors used to explain conceptual change and emphasize that models have to be explicitly related to methods of inquiry in order for taking different aspects, such as the context dependence of concepts and emotional factors, into account.

Gale Sinatra argues for a multi-faceted view of conceptual change learning that takes into account cognitive, affective, situational, and motivational factors and she points out the importance of the learner’s characteristics in the interactive process of conceptual change.

Gregg Solomon discusses, from the perspective of a program director at an agency that funds research on learning and instruction, missteps commonly seen in research proposals looking at conceptual change and its educational implications.

Patricia A. Alexander and Daniel L. Dinsmore visit the literature on expertise development to address the question how to guide someone from conceptual naiveté to conceptual sophistication.

Marcia C. Linn examines different views on conceptual change and argues for the value of the knowledge integration view for the design of effective instruction.

The discussant, Stella Vosniadou, will attempt to provide an integrated view of the different aspects of conceptual change learning elaborated by the presenters.

Models and metaphors used to explain conceptual change are explored. One common model for describing conceptual change has been that of a conception A being exchanged with another conception B. This was the way Piaget described the child’s acquisition of culturally agreed conceptions in his early works on the child’s conception of the world and the child’s construction of reality. The exchange model was also the one adopted in the rapidly growing research on conceptual change in science that began in the seventies. The predominant idea was that the learner have to abandon commonsense conceptions in favour of scientific ones. In parallel to this exchange model there was also other models advocated. It was argued that we do not always abandon old conceptions when we acquire new scientific ones but rather that the learner has to differentiate between explanations in everyday life and in scientific contexts respectively.

In contemporary research there is almost an agreement that conceptions are embedded in conceptual systems. Still, there is a debate about the concept of concept and what there is that changes in conceptual change. The criticism of constructivist approaches from sociocultural theorists has also resulted in an awareness of the context dependence of concepts. Also, emotional factors have been brought to the fore in accounting for the process of conceptual change. All of this has to be accounted for in modelling the process of conceptual change. Here, different models are discussed and it is argued that models have to be explicitly related to methods of inquiry in order for taking these different aspects into account.

Models and metaphors used to explain conceptual change and the concept of concept are explored. One common model for describing conceptual change has been that of a conception A being exchanged with another conception B. This was the way Piaget described the child’s acquisition of culturally agreed conceptions in his early works on the child’s conception of the world and the child’s construction of reality (Piaget, 1929). Of paramount interest were the radical reconstructions of the child as for example from an initial practical solipsism to the construction of a world which includes her/himself as an element. In Piaget’s empirical work this was manifested in studies on how children alter conceptions of phenomena like dreams, thoughts, the origin of the moon, the cause and the nature of night, etc.

The exchange model was also the one adopted in the rapidly growing research on conceptual change in science that began in the seventies. The metaphor for learning was that of a scientist who falsifies ideas and formulate new hypothesises (Posner, Strike, Hewson, and Gertzog, 1982). The predominant idea was that in learning science the learner had to abandon commonsense conceptions in favour of scientific ones. The process for this exchange of conceptions was however modelled in different ways (Hewson, 1981) and also the concept of conception was conceived of in different ways (diSessa, 1988). In parallel to this exchange model there was also other models advocated. It was argued that we do not always abandon old conceptions when we acquire new scientific ones but rather that the learner has to differentiate between explanations in everyday life and in scientific contexts respectively (Caravita & Halldén, 1994).

In contemporary research there is almost an agreement that conceptions are embedded in conceptual systems and this has to be taken into account in explanations of conceptual change (cf. Tiberghien, 1994; Vosniadou, 1994). However, the nature of these systems is looked upon in different ways and this has implications for the modelling of the process of conceptual change. But still, there is also the debate about the concept of concept and what there is that changes in conceptual change (diSessa & Sherin, 1998). The criticism of constructivist approaches from sociocultural theorists has also resulted in an awareness of the context dependence of concepts. Also, emotional factors have been brought to the fore in accounting for the process of conceptual change (Pintrich, Marx, & Boyle, 1993). Thus, current models tries to account for learning processes involved in conceptual change. Necessary conditions for these processes to come into play are discussed as well as the nature of what there is that changes, i.e. the concept of concept. Also, the context dependence of meaning and meaning making is taken into account. Different models are discussed and it is argued that the models of conceptual change have to be explicitly related to methods of inquiry in order for taking these different aspects into account. An empirical study of young children’s, aged four to five, understanding of the word “earth” is used as an example (Halldén, Larsson, & Haglund, in press).

References

Caravita, S. & Halldén, O. (1994). Re-framing the problem of conceptual change. Learning and Instruction, 4, 89-112.

diSessa, A.A. (1988). Knowledge in pieces. In G. Forman & P. Pufall (Eds.), Constructivism in the computer age, 49-70. Mahwah, NJ: Lawrence Erlbaum.

diSessa, A. A. & Sherin. (1998), What changes in conceptual change? International Journal of Science Education, 20, 1155-1191.

Halldén, O., Larsson, Å., & Haglund, L. (in press). On the emergence of a conception: Metaphors and models in research on conceptual change. To appear in S. Vosniadou (Ed.), Handbook on research on conceptual change. Hillsdale: Erlbaum.

Hewson, P. W. (1988). A conceptual change approach to learning science. Journal of Research in Science Education, 3, 383-396.

Piaget, J. (1929/1951). The child’s conception of the world. Maryland: Littlefield Adams Quality Paperbacks.

Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research, 63, 167-199.

Posner, G.J., Strike, K.A., Hewson, P.W., and Gertzog, W.A. (1982). Accomodation of a scientific concept: Toward a theory of conceptual change. Science Education, 66, 211-227.

Tiberghien, A. (1994). Modeling as a basis for analyzing teaching-learning situations. Learning and Instruction, 4, 71-87.

Vosniadou, S. (1994). Capturing and modelling the process of conceptual change. Learning and Instruction, 4, 45-69.

Contact information

Ola.Hallden@ped.su.se

Asa.Larsson@ped.su.se

Lizha@ped.su.se

Department of Education

Stockholm University

SE-106 91 Stockholm

Sweden

Phone number +46 8 162000

The view of conceptual change that is emerging from the warming trend (Sinatra, 2005) is one of a multi-faceted, theoretically complex, and interactive process. Sinatra and Mason (in press) argue that conceptual change should not be examined through only one lens as either a cognitive, developmental, or sociocultural process, but rather multiple lens are needed to understand the complexities of conceptual change learning. Specifically, the acceptance of a multi-faceted view of conceptual change is necessary to advance our understanding of this complex process. Sinatra and Mason (in press) describe conceptual change as ranging from algorithmic (or automatic) to intentional (conscious and deliberate) using a levels of awareness framework (for various characterization of a levels view see Anderson, 1990; 1991; Craik & Lockhart, 1972; Newell, 1990; Stanovich, 1999). Key to understanding this multi-faceted view of conceptual change learning is an examination of the role learner characteristics play in the interactive process of conceptual change. In this presentation I will discuss this multifaceted, multiple lens view of conceptual change learning and I will touch on how individual differences in constructs such as mastery goals, epistemological beliefs, personal interest, importance, values, achievement goals, self-efficacy, and control beliefs can play a determinative role in intentional conceptual change.

Early accounts of conceptual change learning (Clough, & Driver, 1985; Eaton, Anderson, & Smith, 1984; Posner et al., 1982; West & Pines, 1985) gave little recognition to the affective, situational, and motivational factors that often play a determinative role in whether or not a new conception will be adopted. These perspectives were labeled “cold conceptual change” due to their focus on rational, cognitive factors to the exclusion of extra-rational or “hot” constructs (Pintrich, Marx, & Boyle, 1993). Recently, there has been a “warming trend” in conceptual change research (Sinatra, 2005) reflected by an increasing number of researchers characterizing conceptual change as social, contextual, motivational, and affective in nature (see for example, Dole & Sinatra, 1998, Gregoire, 2003, and Murphy & Mason, 2006).

The view of conceptual change that is emerging from the warming trend is one of a multi-faceted, theoretically complex, and interactive process. Sinatra and Mason (in press) argue that conceptual change should not be examined through only one lens as either a cognitive, developmental, or sociocultural process, but rather multiple lens are needed to understand the complexities of conceptual change learning. Specifically, the acceptance of a multi-faceted view of conceptual change is necessary to advance our understanding of this complex process.

Sinatra and Mason (in press) describe conceptual change as ranging from algorithmic (or automatic) to intentional (conscious and deliberate) using a levels of awareness framework (for various characterization of a levels view see Anderson, 1990; 1991; Craik & Lockhart, 1972; Newell, 1990; Stanovich, 1999). Conceptual change learning that occurs automatically or implicitly without the conscious attention of the learner would be characterized by the “ah ha” phenomena. That is, when the new information “clicks” into place and knowledge is restructured without apparent conscious deliberation, and without purposive regulation of learning goals directed toward resolving cognitive conflict, or without specific intentions to change one’s view.

In contrast to this form of conceptual change, some aspects of conceptual change learning have been characterized as intentional (Sinatra & Pintrich, 2003). Sinatra and Pintrich (2003) defined intentional conceptual change as “goal-directed and conscious initiation and regulation of cognitive, metacognitive, and motivational processes to bring about a change in knowledge” (p.6). Unlike algorithmic, or non-intentional conceptual change, intentional conceptual change is under the learner’s conscious control.

Key to understanding this multi-faceted view of conceptual change learning is an examination of the role learner characteristics play in the interactive process of conceptual change. According to Sinatra and Mason (in press), learner characteristics can be brought to bear on functions at the intentional level of awareness. This may resolve (at least in part) the apparent conceptual change learning paradox – that is background knowledge which usually serves a facilitator of learning, can also serve as a barrier to learning. Learner characteristics, such as learning goals, motivations, and intentions to learn (or to not learn) new conceptions may account for differences among students with similar background knowledge.

In this presentation I will discuss this multifaceted, multiple lens view of conceptual change learning and I will touch on how individual differences in constructs such as mastery goals, epistemological beliefs, personal interest, importance, values, achievement goals, self-efficacy, and control beliefs can play a determinative role in intentional conceptual change. Research on learner characteristics that play a key role at the intentional level of awareness in conceptual change has begun to explore exactly how these constructs can determine change, over and above background knowledge. It is the importance of these characteristics for understanding the multifaceted, interactive, theoretically complex nature of change that must be more fully explored to advance our understanding of conceptual change.

Sinatra and Mason (in press) paint a picture of the conceptual change process as complex, interactive, and at once cognitive, affective, and social. Theories and models of conceptual change learning must account for the complexity of the process. Models that are strictly cognitive or strictly social will not ultimately succeed in explaining the complexity of learning that is conceptual change.

Different theoretical perspectives on conceptual change can each be viewed as offering explanations of change at various levels of awareness, stages of development, or points along the individual to social continuum. A view of conceptual change as multifaceted affords an opportunity to bridge the gap between what is oft seen as competing explanations of the change process. Different perspectives broaden our understanding of the nature of change itself and how knowledge restructuring can be facilitated. Ultimately, conceptual change theories and models that embrace the complexity inherent in the knowledge restructuring process will have profound implications for all forms of learning.

Contact Information:

Gale M. Sinatra, Ph.D

Professor, Educational Psychology

University of Nevada, Las Vegas

4505 Maryland Parkway

Las Vegas, Nevada 89154

P-702-895-2605

F-702-895-1658

Within the educational research literature there is wide acceptance of the fact that the conceptual knowledge of experts differs from that of non-experts both quantitatively and qualitatively. There is also acknowledgment that experts and novices may approach domain-specific problems in distinct ways or may treat anomalous data differently. Even the typical mis-conceptions of experts and non-experts are presumed to vary significantly.

Although the expertise research has been fertile ground for ascertaining the apparent contrasts in mental representations, problem-solving strategies, and misconceptions between true novices and acknowledged experts, that rich literature has been less useful in understanding how to support conceptual development in those journeying toward expertise. In essence, simply knowing that experts and novices are different does not necessarily tell us how to guide someone from conceptual naiveté to conceptual sophistication in any domain.

In this presentation, we will revisit the extant literature on expertise development, as framed within the Model of Domain Learning (MDL) to address this “how” question. Specifically, we will explore how the MDL can inform efforts to facilitate conceptual development in less expert populations through simultaneous attention to principled knowledge, strategic processing, and motivational factors, especially individual interest.

Within the educational research literature there is wide acceptance of the fact that the conceptual knowledge of experts differs from that of non-experts both quantitatively and qualitatively (Chi, Feltovich, & Glaser, 1981). There is also acknowledgment that experts and novices may approach domain-specific problems in distinct ways or may treat anomalous data differently (Chi, Glaser, & Farr, 1988; Chinn & Brewer, 1993). Even the typical mis-conceptions of experts and non-experts are presumed to vary significantly (Alexander, 1998; Perkins & Simmons, 1988).

Although the expertise research has been fertile ground for ascertaining the apparent contrasts in mental representations, problem-solving strategies, and misconceptions between true novices and acknowledged experts, that rich literature has been less useful in understanding how to support conceptual development in those journeying toward expertise. In essence, simply knowing that experts and novices are different does not necessarily tell us how to guide someone from conceptual naiveté to conceptual sophistication in any domain.

In this presentation, we will revisit the extant literature on expertise development, as framed within the Model of Domain Learning (MDL; Alexander, 1997, 2003) to address this “how” question. Specifically, we will explore how the MDL can inform efforts to facilitate conceptual development in less expert populations through simultaneous attention to principled knowledge, strategic processing, and motivational factors, especially individual interest.

Knowledge in the MDL consists of both domain knowledge and topic knowledge. Topic knowledge (depth of knowledge) can be thought of as concepts about particular topics in a given domain. Domain knowledge (breadth of knowledge) refers to the interrelatedness of topic knowledge to the domain as a whole. Meaning for the expert, and novice for that matter, is dependent on the quality of relations between concepts in the domain (Pines, 1985).

The MDL also divides strategic processing into two distinct, qualitatively different processes. Novices and experts trying to accrete, tune, or restructure knowledge need to rely on two types of processes, surface -level strategies and deep-processing strategies. While novices rely heavily on surface level strategies (e.g. problem identification), experts are able to rely more heavily on deep-processing strategies (e.g., problem transformation; Alexander, et. al., 1994).

Lastly, the MDL acknowledges that motivation also plays an important role in the journey toward expertise. Both situational interest and individual interest are key elements in the development of expertise. Situational interest is primarily helpful in the early stages of knowledge acquisition. However, more importantly for the development of expertise, individual interest is the sustaining force necessary to propel one into high competence or expertise and to sustain those who attain expertise. The presentation will not only situate conceptual change within these three dimensions (i.e. knowledge, strategies, and interest), but will also touch on the interactions of these three dimensions.

References

Alexander, P. A. (1997b). Mapping the multidimensional nature of domain learning: The interplay of cognitive, motivational, and strategic forces. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement (Vol. 10, pp. 213-250). Greenwich, CT: JAI Press.

Alexander, P. A. (1998). The nature of disciplinary and domain learning: The knowledge, interest, and strategic dimensions of learning from subject-matter text. In C. Hynd (Ed.), Learning from text across conceptual domains (pp. 263-287). Mahwah, NJ: Lawrence Erlbaum Associates.

Alexander, P. A. (2003). The development of expertise: The journey from acclimation to proficiency. Educational Researcher, 32(8), 10-14.

Alexander, P.A., Kulikowich, J.M., & Jetton, T.L. (1994). The role of subject-matter knowledge and interest in the processing of linear and nonlinear texts. Review of Educational Research, 64, 201-252.

Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

Chi, M. T. H., Glaser, R., & Farr, M. (1988). The nature of expertise. Hillsdale, NJ: Erlbaum. Ericsson, K. A., & Smith, J. (1991). Toward a general theory of expertise: Prospects and limits. New York: Cambridge University Press.

Perkins, D. N., & Simmons, R. (1988). Patterns of misunderstanding: An integrative model for science, math, and programming. Review of Educational Research, 58, 303-326.

Pines, A.L. (1985). Toward a taxonomy of conceptual relations and the implications for the evaluation of cognitive structures. In West, L.H.T. & Pines, A.L. (Eds.), Cognitive structure and conceptual change (pp. 101-116). Orlando, FL: Academic Press.

Patricia A. Alexander [palexand@umd.edu, (301) 405-2821]

Daniel L. Dinsmore [dinsmore@umd.edu, (301) 405-6956)]

Department of Human Development

University of Maryland

3304F Benjamin Building

College Park, MD 20742

The nature of conceptual change is contested. Some see students following a trajectory consisting of the accumulation of knowledge, consistent with the lecture method of instruction. Others see conceptual change as constrained by developmental processes and perhaps unresponsive to instruction. Recently an emerging view of conceptual change focuses on the broad range of ideas that students articulate. My research on the knowledge integration perspective argues that conceptual change results from efforts to build on ideas that students generate spontaneously, opportunities to add ideas that stimulate reconsideration of existing ideas, efforts to build criteria that distinguish idea, and opportunities to reflect on the mix of ideas. Students who engage in knowledge integration seek to add new ideas, actively sort out their ideas, take advantage of evidence from a range of sources including their personal experiences, and deliberately attempt to build coherent understanding.  Contrasting these views of conceptual change raises some issues that all the theories of conceptual change need to address including the role of memory and forgetting, problem context, intuitive beliefs, grain size of the ideas, and successful instruction. In this paper I discuss these issues and seek to integrate the varied perspectives on conceptual change.

This paper contrasts perspectives on conceptual change and interprets them through the lens of instruction in science. For the purposes of this paper, conceptual change is identified as the individuals’ lifelong trajectory of understanding of a given topic or discipline.

Researchers disagree about the nature of conceptual change. One group views conceptual change as the accumulation of knowledge and advocates a transmission model of instruction (Anderson, 1986; Bjork, 1999).

Developmental theorists look at the lifelong trajectories of student understanding, paying attention to the rate of change in reasoning and the similarities of reasoning at specific ages (Piaget, 1953). These researchers seek to explain the origin of specific ideas and also to predict periods of gradual or rapid change (Carey, 1985; Chi & Roscoe, 2002; Slotta & Chi, 2006; Vosniadou, 2002). Some developmental theorists see little role for formal instruction while others advocate direct instruction.

An emerging group of researchers argues that students build varied, contradictory, and rich ideas that exist alongside each other in a repertoire (diSessa, 1988; Linn, 1995; Metz, 1991; Siegler, 1996; Songer, 1996; White and Fredrickson, 1998).

• diSessa (1988) advocates a “knowledge in pieces” view and describes explanations that students generate (such as “sound dies out” or “Heavy objects fall faster than light objects," "Things bounce because they are 'springy,'" and "Continuing force is needed for continuing motion") as pheonomenological primitives to capture their iconic and descriptive nature.


• The knowledge integration view draws on longitudinal research (Clark & Linn, 2003; Linn & Hsi, 2000) to illustrate how students spontaneously generate ideas and to show how they respond to new ideas during instruction as well as how they continue to combine and organize their ideas over time.

This group generally argues that the goal of instruction is to encourage students to inspect, distinguish, and evaluate their ideas in a way that leads to some form of reconciliation. The knowledge integration perspective shows how technology-enhanced instruction can harness the reasoning students engage in to generate spontaneous ideas by adding pivotal cases (Linn, 2005) that stimulate comparisons among ideas and by scaffolding the process of sorting out ideas.

Each perspective on conceptual change offers some supportive evidence and neglects some issues that other perspectives emphasize. Merging these three approaches to study of conceptual change raises a set of issues that a comprehensive view should address. A successful perspective should explain all the evidence concerning conceptual change including at least the following:

• Research on memory and forgetting that shows the benefit of spaced practice and the value of generation rather than selection in learning activities.


• Research on the development of the individual and the trajectory of lifelong learning, including evidence for gradual or rapid change and accounts of the origins of misconceptions, alternative conceptions, beliefs, and constructed ideas


• Research on the role of the context of a problem or idea in impacting student learning and performance 


• Research on how learners and experts connect the varied levels of analysis of scientific phenomena to each other


• Research showing that some instructional designs (like visualizations and reflection activities) are more successful than others (like drill on facts).

This presentation explores the five issues above from the standpoint of the main formulations of conceptual change. The presentation focuses on implications for instruction and provides evidence for the value of the knowledge integration view. 

References

Anderson, R. C. (1986). Some reflections on the acquisition of knowledge. Educational Researcher, 13(5), 5-10.

Bjork, R. A. (1999). Assessing our own competence: Heuristics and illusions. In D. Gopher & A. Koriat (Eds.), Attention and performance XVII.  Cognitive regulation of performance: Interaction of theory and application (pp. 435-459). Cambridge, MA: MIT Press.

Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: MIT Press.

Chi, M. T. H., & Roscoe, R. D. (2002). The process and challenges of conceptual change. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 3-27). Netherlands: Kluwer Academic Publishers.

Clark, D. B., & Linn, M. C. (2003). Scaffolding knowledge integration through curricular depth. Journal of Learning Sciences, 12(4), 451-494.

diSessa, A. A. (1998). Knowledge in pieces. In G. Forman & P. Puffall (Eds.), Constructivism in the computer age (pp. 49-70). Hillsdale, NJ: Lawrence Erlbaum Associates.

Linn, M. C. (1995). Designing computer learning environments for engineering and computer science: The scaffolded knowledge integration framework. Journal of Science Education and Technology, 4(2), 103-126.

Linn, M. C. (2005). WISE design for lifelong learning-Pivotal Cases. In P. Gärdenfors & P. Johansson (Eds.), Cognition, education and communication technology (pp. 223-256). Mahwah, NJ: Lawrence Erlbaum Associates.

Linn, M. C., & Hsi, S. (2000). Computers, teachers, peers: Science learning partners. Mahwah, NJ: Lawrence Erlbaum Associates.

Metz, K. E. (1991). Development of explanation: Incremental and fundamental change in children's physics knowledge. Journal of Research in Science Teaching (Special Issue: Students’ models and epistemologies), 28(9), 785-797.

Piaget, J. (1953). The origin of intelligence in the child. London,: Routledge & Paul.

Siegler, R. S. (1996). Emerging minds: The process of change in children's thinking. New York: Oxford University Press.

Slotta, J. D., & Chi, M. T. H. (2006). Helping students understand challenging topics in science through ontology training. Cognition and Instruction, 24(2), 261-289.

Songer, N. B. (1996). Exploring learning opportunities in coordinated network-enhanced classrooms - A case of kids as global scientists. Journal of the Learning Sciences, 5(4), 297-327.

Vosniadou, S. (2002). On the nature of naïve physics. In M. Limón & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 61-76). Dordrecht: Kluwer Academic Publishers.

White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3-118.

Contact information

Professor Marcia C. Linn

4611 Tolman Hall

Education in Mathematics, Science, and Technology

University of California, Berkeley

Berkeley, CA 94720-1670

PHONE    510 6436379

FAX           510 6430520

EMAIL      mclinn@berkeley.edu