Using causal networks to represent the targets of resource coordination

TL;DR

This paper introduces causal network diagrams as a tool to represent and analyze how students coordinate and refine their understanding of physics resources, specifically in projectile motion.

Contribution

It presents causal network diagrams as a new theoretical tool for representing the coordination of resources in physics learning.

Findings

Causal networks reveal the types of resource coordination needed for correct physics understanding.

They illustrate how students integrate causal influences and use qualitative logic.

The approach clarifies the process of aligning student reasoning with canonical physics concepts.

Abstract

The resources framework emphasizes the potential productivity of student intuitions for constructing a canonical understanding of physics. It models learning as the progressive coordination and refinement of these 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 the targets of research coordination. As an example, we compare student reasoning about projectile motion to the causal network describing that motion. We argue that the causal networks make manifest and explicit two types of resource coordination required to achieve a correct physical understanding: (i) integrating additional causal influences and mediators and (ii) using qualitative logic to draw valid inferences.