Toward a modeling theory of physics instruction

Hestenes, David

doi:10.1119/1.15129

Cited by 435 publications

(370 citation statements)

References 24 publications

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“…Constraints must necessarily be true for the cause-effect relation to be instantiated. The difference between contexts and constraints is similar to that proposed by Hestenes (1987) between "state variables" and "object variables".…”

Section: Knowledge Representation Frameworksupporting

confidence: 54%

“…A system of priorities determines order in reasoning with p-prims. The knowledge sparseness view is shared by Hestenes (1987), who also claims that ". .…”

Section: Education and Cognitive Sciencesmentioning

confidence: 99%

“…Several studies in education research report on students' difficulties in learning Physics [Driver, Guesne, & Tiberghien, 1985;Halloun & Hestenes, 1987;Hestenes, 1987;Duit, 1995;diSessa, 1996]. We believe that the interpretation of these difficulties, through their modelization, may produce valuable insights into human learning mechanisms.…”

Section: Domain Of Investigation: Basic Concepts and Qualitative Relamentioning

confidence: 99%

“…In particular, we are interested in teaching/learning Physics, domain in which the separation between problem solving and explanation ability is striking [Hestenes, 1987;Tiberghien, 1994;Duit, 1995].…”

Section: Introductionmentioning

confidence: 99%

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Neri

2000

Machine Learning

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Abstract. The goal of the reported research is the development of a computational approach that could help a cognitive scientist to interactively represent a learner's mental models, and to automatically validate their coherence with respect to the available experimental data. In a reported case-study, the student's mental models are inferred from questionnaires and interviews collected during a sequence of teaching sessions. These putative cognitive models are based on a theory of knowledge representation, derived from psychological results and educational studies, which accounts for the evolution of the student's knowledge over a learning period. The learning system WHY, able to handle (causal) domain knowledge, shows how to model the answers and the causal explanations given by the learner.

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Section: Knowledge Representation Frameworksupporting

confidence: 54%

“…A system of priorities determines order in reasoning with p-prims. The knowledge sparseness view is shared by Hestenes (1987), who also claims that ". .…”

Section: Education and Cognitive Sciencesmentioning

confidence: 99%

Section: Domain Of Investigation: Basic Concepts and Qualitative Relamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neri

2000

Machine Learning

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“…In Hestenes's and Halloun's approaches, the focus is on learning a selected number of basic models in order to acquire Bthe essential factual and procedural knowledge^ (Hestenes 1987; see also, Halloun 2007). The structure of physics knowledge is then seen to comprise of these families of models together with the theory and principles for relating the models and constructing them from the theory (Halloun 1996;Hestenes 1992;Wells et al 1995).…”

Section: Constructive Modeling In Science Educationmentioning

confidence: 99%

Models as Relational Categories

Kokkonen

2017

Sci & Educ

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Model-based learning (MBL) has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL (using ready-made models, constructive modeling, and generative modeling) and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students' mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students' mental representations of models in terms of evolving relational structures in greater detail than previously done.

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Using scientific inquiry activities in exhibit explanations

Allen

1997

Sci. Ed.

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This study investigated the effect of different scientific inquiry activities on visitors' understanding of the science underlying an interactive exhibit. The exhibit, “colored shadows,” creates a pattern of colored shadows on a white wall, due to a person's body blocking the light from colored lamps. The subjects were 392 museum visitors, aged 7 to adult. They were individually guided through a structured interview, during which they did one of seven inquiry activities, randomly assigned. The activities were: generate an explanation; interpret an explanation; troubleshoot an explanation; choose between two explanations; choose plus design a discriminating experiment; choose plus make and test a related prediction; and make a prediction before experiencing the phenomenon. As a test of their final understanding, visitors were asked to complete two near‐transfer tasks in diagrammatic form. The results showed that the interpretation activity was the most effective in facilitating visitors' understanding of the mechanism of shadow‐creation; least effective was the activity in which visitors made a prediction before experiencing the phenomenon. Visitors had relatively little difficulty choosing correctly from two explanations, but had much more difficulty designing a discriminating experiment. Only rarely during the interviews did visitors revise their thinking in the face of disconfirming evidence. Although the majority of visitors did not answer both transfer problems correctly, their thinking generally showed consistency and logic. The most common types of reasoning used were causal models that traced the path of light from each lamp to the object and to the wall. Such models incorporated ideas of reflection, blocking, and reflection/blocking hybrids, among others. A follow‐up study will incorporate these insights into stand‐alone exhibit labels for further testing. © 1997 John Wiley & Sons, Inc. Sci Ed 81:715–734, 1997.

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Toward a modeling theory of physics instruction

Cited by 435 publications

References 24 publications

Untitled

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Models as Relational Categories

Using scientific inquiry activities in exhibit explanations

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