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Abstract Title: Using causal networks to map out the targets of resource coordination
Abstract: The resources framework emphasizes the potential productivity of students' intuitions for constructing a canonical understanding of physics. It models learning as the progressive coordination and refinement of these intuitive resources. Yet, there is a lack of theoretical clarity about how resources should be coordinated and refined to align with canonical physics. We present causal network diagrams as a tool for representing physics learning targets in terms of resource coordination. As an example, we compare student reasoning on projectile motion questions to the causal network describing the physical model of projectile motion. The causal network makes manifest and explicit three types of resource coordination required to construct physical understandings aligned with canonical physics: (i) integration of unreliable resources into correct physical models, (ii) joint activation of multiple resources, and (iii) determination of valid qualitative inferences from multiple resources.
Abstract Type: Contributed Poster Presentation
Session Time: Poster Session II
Poster Number: B75

Author/Organizer Information

Primary Contact: Eric Kuo
University of Pittsburgh
and Co-Presenter(s)
Nolan Weinlader, University of Pittsburgh
Benjamin M. Rottman, University of Pittsburgh
Timothy J. Nokes-Malach, University of Pittsburgh