ANALOGIES - KAPON · Analogical Reasoning and Conceptual Change in Physics Education

Analogical Reasoning and Conceptual Change in Physics Education

Project context and objectives Conceptual change is a core feature of learning science. It reflects the knowledge transformation and development that occurs during the learning process. Educational researchers have shown that a key difficulty in teaching science, particularly physics, is how to bring about conceptual change; namely how to lead students to accept scientific conceptualisations that often differ from experiences students have on the scale of their everyday experience or their naive views of phenomena (Duit and Treagust, 2003). Analogical reasoning plays a central role in the process of conceptual change, which is a key part of scientific discovery (e.g. Dunbar, 1997; Gentner et al., 1997). The instruction of complex concepts in science, and especially in physics, often involves some use of analogies (Dagher, 1995), and educational researchers have argued that analogies can guide students towards conceptual change (e.g. Brown and Clement, 1989; Duit, Roth, Komorek and Wilbers, 2001). The project goal is to understand how. The theoretical framework that guides this project is the 'knowledge in pieces' perspective on conceptual change (diSessa, 1993). The objectives are: - to develop an empirically based theory that explains how and to what extent instructional analogies affect conceptual change during the acquisition of new knowledge in physics; - to derive practical recommendations from this theory regarding the use of analogical reasoning when teaching science, and particularly when teaching physics in high school and at the introductory undergraduate level. Work performed During the outgoing period of this research, six clinical interviews (diSessa, 2007) were conducted with high school students using an enriched and elaborated version of Clement and Brown's bridging tutoring sequence of the existence of the normal force. Changes and additions were implemented to provide better triangulation of the students' particular knowledge structures and the contexts in which they were used. Changes included additional analogies, and experimenting with the physical artefacts employed in the analogies (springs, flexible boards, etc.). A model of explanations and change in explanations was developed through a bottom-up analysis with high temporal resolution. The model draws on diSessa's (1993) model of p-prims and focuses on core elements that provide those judging with an explanation that has a sense of satisfaction. The model is used to explain why a well-known canonical instructional sequence in physics (Minstrell, 1982) is so effective (Kapon and diSessa, 2010) to account for individual differences in response to instructional analogical sequences (Kapon and diSessa, 2010, 2012), and to account for aspects in the emergence of novel knowledge structures prompted by instructional analogical sequences (Kapon and diSessa, 2012). The evaluation of the degree to which the candidate's transferred knowledge is applicable to the target domain and whether the analogical inference seems plausible are acknowledged as important aspects in cognitive models of analogies. However, these models consider evaluation as mediated chiefly by structural similarity across domains (Falkenhainer, Forbus and Gentner, 1986) and pragmatic goals (Holyoak and Thagard, 1989). An important finding emerging from the current research is that the activation of prior knowledge in the form of simple schemes, termed explanatory primitives, strongly affects the learner's acceptance or rejection of an analogical inference. The findings also highlight the advantages of knowledge analysis as a research method in educational research, and enhance our understanding of the nature of effective instruction. Main results The above analysis raises additional, detailed questions about reasoning prompted by instructional analogies. For instance, what aspects of the interaction with the instructor, peers and objects in the learning environment activate a particular explanatory primitive, convince a learner to shift preferences from one primitive to another, and later use the primitive in the construction of new understanding? Such questions are critical for generating instructional implications and suggest that augmenting knowledge analysis with interaction analysis (e.g. Goodwin, 2000) can promote our understanding of how instructional analogies can lead to conceptual change, as well as help us to generate effective instruction (Kapon, 2012; Kapon, in preparation).

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