Designing Simulation Module to Diagnose Misconceptions in Learning Natural Selection

Man Su, J. Yohan Cho, Michelene T.H. Chi, Nicole Boucher, Brandon Vanbibber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationISLS Annual Meeting 2021 Reflecting the Past and Embracing the Future - 15th International Conference of the Learning Sciences, ICLS 2021
EditorsErica de Vries, Yotam Hod, June Ahn
PublisherInternational Society of the Learning Sciences (ISLS)
Pages410-417
Number of pages8
ISBN (Electronic)9781737330615
StatePublished - 2021
Externally publishedYes
Event15th International Conference of the Learning Sciences, ICLS 2021 - Virtual, Online
Duration: Jun 8 2021Jun 11 2021

Publication series

NameProceedings of International Conference of the Learning Sciences, ICLS
ISSN (Print)1814-9316

Conference

Conference15th International Conference of the Learning Sciences, ICLS 2021
CityVirtual, Online
Period6/8/216/11/21

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Education

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