Enhancing understanding of population genetics through automated modeling and simulation

Elena V. Komarova

doi:10.55056/cte.735

Authors

Elena V. Komarova Independent researcher https://orcid.org/0000-0002-3476-3351

DOI:

https://doi.org/10.55056/cte.735

Keywords:

population genetics education, computational modeling, Hardy-Weinberg equilibrium, technical replication, biological replication, science education, simulation-based learning, automated experimentation, student misconceptions, genetic literacy

Abstract

Understanding population genetics concepts, particularly the Hardy-Weinberg equilibrium law, presents significant challenges to students at various educational levels. Our research addresses methodological and practical limitations in teaching these concepts through educational model experiments. We document the evolution of an instructional approach for studying the Hardy-Weinberg law, progressing from material models with manual calculations to a fully automated simulation system. The enhanced methodology allows for substantial increases in model population size and facilitates both technical and biological replication in the educational context. Through iterative design and testing with undergraduate students between 2015-2023, we demonstrate that automated modeling significantly enhances students' conceptual understanding by visualizing abstract genetic-evolutionary processes. Our approach overcomes traditional experimental limitations of mass sampling and replication while making complex population-level phenomena accessible to students. Survey results indicate improved comprehension of genetic equilibrium concepts, with increasing student engagement in exploring evolutionary mechanisms. This work contributes to the theoretical integration of computational modeling and science education by establishing a pedagogical framework that connects abstract genetic concepts to their practical application, promoting deeper scientific literacy through guided simulation experiences.

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