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In this study, we have explored the effectiveness of two instructional approaches in the context of the motion of objects falling at terminal speed in the presence of air resistance. We ground these instructional approaches in dual-process theories of reasoning, which assert that human cognition relies on two thinking processes. Dual-process theories suggest multiple possible avenues by which instruction might impact student reasoning. In this paper, we compare two possible instructional approaches: one designed to reinforce the normative approach (improving the outputs of the intuitive process) and another that guides students to reflect on and analyze their initial ideas (supporting the analytic process). The results suggest that for students who have already demonstrated a minimum level of requisite knowledge, instruction that supports analysis of their likely intuitive mental model leads to greater learning benefits in the short term than instruction that focuses solely on providing practice with the normative mindware. These results have implications for the design of instructional materials and help to demonstrate how dual-process theories can be leveraged to explain the success of existing research-based materials. Published by the American Physical Society2024 

Over the course of the introductory calculus-based physics course, students are often expected to build conceptual understanding and develop and refine skills in problem solving and qualitative inferential reasoning. Many of the research-based materials developed over the past 30 years by the physics education research community use sequences of scaffolded questions to step students through a qualitative inferential reasoning chain. It is often tacitly assumed that, in addition to building conceptual understanding, such materials improve qualitative reasoning skills. However, clear documentation of the impact of such materials on qualitative reasoning skills is critical. New methodologies are needed to better study reasoning processes and to disentangle, to the extent possible, processes related to physics content from processes general to all human reasoning. As a result, we have employed network analysis methodologies to examine student responses to reasoning-related tasks in order to gain deeper insight into the nature of student reasoning in physics. In this paper, we show that network analysis metrics are both interpretable and valuable when applied to student reasoning data generated from . We also demonstrate that documentation of improvements in the articulation of specific lines of reasoning can be obtained from a network analysis of responses to reasoning chain construction tasks. Published by the American Physical Society2024