Emerging patterns of understanding: tracing patterns in students’ conceptions of evolution using machine learning

Abstract Background Evolution is at the core of modern biology education, yet students continue to struggle to form explanations of evolutionary phenomena. Formative assessment has the potential to support students in their learning, but comprehensive assessment of the concepts and preconceptions students hold presents teachers with difficulties. In this study, we analyze patterns of concepts and preconceptions in upper-secondary level students' explanations of evolutionary phenomena. We investigate how the patterns develop, which interdependencies between concepts and preconceptions we can observe and in which way the findings diverge from the intended sequence of taught concepts. Method We analyzed the explanations students gave to the ACORNS instrument when implemented at five time points during a unit on five factors of evolution using the EvoGrader tool. We constructed binary patterns indicating presence and absence of six key concepts and three preconceptions. Using these patterns, we traced the development of students’ explanations over the course of the unit. Results In total, we found 95 of 512 possible different explanatory patterns in 1662 student answers. We analyzed the patterns for frequency and qualitatively analyzed those patterns that were (1) particularly frequent in at least one time point, (2) showed a directed change of frequency over the course of the unit, (3) differed in frequency between animal/gain and plant/loss contexts, or (4) contained a particularly high number of key concepts or preconceptions. We compared the patterns against established learning progressions to determine students’ conceptual progress in relation to the progression underlying the unit design. Conclusions Our findings highlight not only how a nuanced analysis of student responses can provide instructionally actionable information, but also how crucial elements of student ideas are missed when student explanations are graded as sum scores. We discuss the implications of these findings for formative assessment practices in evolution education.

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Publication Details

Journal
Evolution Education and Outreach
Published
2025-12-03
DOI
https://doi.org/10.1186/s12052-025-00226-3
Primary Topic
Evolution and Science Education
Type
article
Field-Weighted Citation Impact
0.00

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article

Emerging patterns of understanding: tracing patterns in students’ conceptions of evolution using machine learning

Daniela Fiedler, Berrit Katharina Czinczel, Ute Harms, Erin Marie Furtak
Evolution Education and Outreach
Evolution and Science Education
article

Emerging patterns of understanding: tracing patterns in students’ conceptions of evolution using machine learning

Daniela Fiedler, Berrit Katharina Czinczel, Ute Harms, Erin Marie Furtak
article en

Abstract

Abstract Background Evolution is at the core of modern biology education, yet students continue to struggle to form explanations of evolutionary phenomena. Formative assessment has the potential to support students in their learning, but comprehensive assessment of the concepts and preconceptions students hold presents teachers with difficulties. In this study, we analyze patterns of concepts and preconceptions in upper-secondary level students' explanations of evolutionary phenomena. We investigate how the patterns develop, which interdependencies between concepts and preconceptions we can observe and in which way the findings diverge from the intended sequence of taught concepts. Method We analyzed the explanations students gave to the ACORNS instrument when implemented at five time points during a unit on five factors of evolution using the EvoGrader tool. We constructed binary patterns indicating presence and absence of six key concepts and three preconceptions. Using these patterns, we traced the development of students’ explanations over the course of the unit. Results In total, we found 95 of 512 possible different explanatory patterns in 1662 student answers. We analyzed the patterns for frequency and qualitatively analyzed those patterns that were (1) particularly frequent in at least one time point, (2) showed a directed change of frequency over the course of the unit, (3) differed in frequency between animal/gain and plant/loss contexts, or (4) contained a particularly high number of key concepts or preconceptions. We compared the patterns against established learning progressions to determine students’ conceptual progress in relation to the progression underlying the unit design. Conclusions Our findings highlight not only how a nuanced analysis of student responses can provide instructionally actionable information, but also how crucial elements of student ideas are missed when student explanations are graded as sum scores. We discuss the implications of these findings for formative assessment practices in evolution education.

Evolution Education and OutreachVol. 18(1)
University of Copenhagen (DK), University of Colorado Boulder (US), Leibniz Institute for Science and Mathematics Education (DE)
Leibniz-Gemeinschaft
Openalex Percentile: Top 56%
Evolution and Science Education
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