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Title: Designing Simulation Module to Diagnose Misconceptions in Learning Natural Selection
Authors: Su, Man
Cho, J.Yohan
Chi, Michelene T. H.
Boucher, Nicole
Vanbibber, Brandon
Keywords: Learning Sciences
Issue Date: Jun-2021
Publisher: International Society of the Learning Sciences
Citation: Su, M., Cho, J., Chi, M. T., Boucher, N., & Vanbibber, B. (2021). Designing Simulation Module to Diagnose Misconceptions in Learning Natural Selection. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021. (pp. 410-417). Bochum, Germany: International Society of the Learning Sciences.
Abstract: This online experiment, involving 28 high school students, investigates frequencies and types of misconceptions while learning natural selection via two types of simulation modules. The experimental group uses an agent-based model which characterizes Pattern, Agents, Interactions, Relations, and Causality (PAIR-C) features while the control group employs a commonly used PhET simulation. Although the pre-posttest does not capture significant differences between the two conditions, a set of non-leading prompt questions embedded in both simulation modules successfully captured the differences. Students from the experimental condition revealed fewer frequencies and categories of misconceptions and scored significantly higher in explaining one type of common misconceptions as well as responding to objective prompts than the control condition. Our finding indicates that the PAIR-C simulation module might have a better effect in reducing misconceptions. This study manifests strong potential in using a well-structured online simulation module to diagnose and address students’ misconceptions in learning natural selection.
Appears in Collections:ISLS Annual Meeting 2021

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