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Investigating the Efficacy of an Ontological Framework for Teaching Natural Selection Using Agent-Based Simulations

  • Man Su
  • , Michelene T.H. Chi
  • , Jesse Ha
  • , Yue Xin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Integrating agent-based models (ABMs) has been a popular approach for teaching emergent science concepts. However, students continue to find it difficult to explain the emergent process of natural selection. In this study, we employ an ontological framework-the Pattern, Agents, Interactions, Relations, and Causality (PAIR-C)-to guide the design of the ABM simulation module. This study examines the effects of the PAIR-C ABM module versus the Regular ABM module on fostering students' understanding of natural selection. Drawing on pre-posttest data, we found that students in the Intervention group had a better causal understanding when explaining natural selection than the Control group. This paper sheds light on applying an innovative framework to designing effective agent-based simulation modules to teach emergent science concepts.

Original languageEnglish
Title of host publicationISLS Annual Meeting 2023
Subtitle of host publicationBuilding Knowledge and Sustaining our Community - 17th International Conference of the Learning Sciences, ICLS 2023, Proceedings
EditorsPaulo Blikstein, Jan Van Aalst, Rita Kizito, Karen Brennan
PublisherInternational Society of the Learning Sciences (ISLS)
Pages106-113
Number of pages8
ISBN (Electronic)9781737330677
StatePublished - 2023
Event17th International Conference of the Learning Sciences, ICLS 2023 - Montreal, Canada
Duration: 10 Jun 202315 Jun 2023

Publication series

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

Conference

Conference17th International Conference of the Learning Sciences, ICLS 2023
Country/TerritoryCanada
CityMontreal
Period10/06/2315/06/23

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