Characterizing Natural Selection Contextual Transfer with Epistemic Network Analysis: A Case for Unplugged Computational Thinking.
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| Title: | Characterizing Natural Selection Contextual Transfer with Epistemic Network Analysis: A Case for Unplugged Computational Thinking. |
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| Authors: | Peel, Amanda1 (AUTHOR) apeel@nmsu.edu, Arastoopour Irgens, Golnaz2 (AUTHOR) |
| Source: | Journal of Science Education & Technology. Oct2025, Vol. 34 Issue 5, p1055-1067. 13p. |
| Subject Terms: | *Contextual learning, *Facilitated learning, *Education methodology, *Scholars, *Mixed methods research, Natural selection, Systems theory |
| Abstract: | Evolution is a key biological concept, and natural selection is an important mechanism of evolution, but studies indicate students reason about natural selection differently based on organismal context. This paper investigates students' explanations of natural selection in varying contexts after a computational thinking (CT)-central unit designed to scaffold natural selection transfer. The research questions address natural selection change, contextual differences in students' explanations, and patterns of cooccurrences in students' natural selection explanations. Students learned about natural selection through scaffolded transfer via Computational Thinking through Algorithmic Explanations (CT-AE), an unplugged instructional approach. The data source is students' explanations of four pre- and post-unit natural selection scenarios about bacteria, mice, lilies, and mosquitos. This mixed methods study included nonparametric statistics to determine differences between contexts in post-unit explanations and Epistemic Network Analysis (ENA) to create and compare networks of co-occurrences in students' explanations. There were significant differences between the four pre-unit scenario explanations, but the post-unit explanations displayed fewer differences. ENA analysis indicated that student responses for each scenario were not significantly different. These trends indicate students' explanations of natural selection based on context varied less after the unit. These results suggest that the unit was successful in scaffolding transfer of natural selection context across contexts. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Science Education & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 189590671 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Characterizing Natural Selection Contextual Transfer with Epistemic Network Analysis: A Case for Unplugged Computational Thinking. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Peel%2C+Amanda%22">Peel, Amanda</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> apeel@nmsu.edu</i><br /><searchLink fieldCode="AR" term="%22Arastoopour+Irgens%2C+Golnaz%22">Arastoopour Irgens, Golnaz</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Science+Education+%26+Technology%22">Journal of Science Education & Technology</searchLink>. Oct2025, Vol. 34 Issue 5, p1055-1067. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Contextual+learning%22">Contextual learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Facilitated+learning%22">Facilitated learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Education+methodology%22">Education methodology</searchLink><br />*<searchLink fieldCode="DE" term="%22Scholars%22">Scholars</searchLink><br />*<searchLink fieldCode="DE" term="%22Mixed+methods+research%22">Mixed methods research</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+selection%22">Natural selection</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+theory%22">Systems theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Evolution is a key biological concept, and natural selection is an important mechanism of evolution, but studies indicate students reason about natural selection differently based on organismal context. This paper investigates students' explanations of natural selection in varying contexts after a computational thinking (CT)-central unit designed to scaffold natural selection transfer. The research questions address natural selection change, contextual differences in students' explanations, and patterns of cooccurrences in students' natural selection explanations. Students learned about natural selection through scaffolded transfer via Computational Thinking through Algorithmic Explanations (CT-AE), an unplugged instructional approach. The data source is students' explanations of four pre- and post-unit natural selection scenarios about bacteria, mice, lilies, and mosquitos. This mixed methods study included nonparametric statistics to determine differences between contexts in post-unit explanations and Epistemic Network Analysis (ENA) to create and compare networks of co-occurrences in students' explanations. There were significant differences between the four pre-unit scenario explanations, but the post-unit explanations displayed fewer differences. ENA analysis indicated that student responses for each scenario were not significantly different. These trends indicate students' explanations of natural selection based on context varied less after the unit. These results suggest that the unit was successful in scaffolding transfer of natural selection context across contexts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Science Education & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=189590671 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10956-024-10185-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1055 Subjects: – SubjectFull: Contextual learning Type: general – SubjectFull: Facilitated learning Type: general – SubjectFull: Education methodology Type: general – SubjectFull: Scholars Type: general – SubjectFull: Mixed methods research Type: general – SubjectFull: Natural selection Type: general – SubjectFull: Systems theory Type: general Titles: – TitleFull: Characterizing Natural Selection Contextual Transfer with Epistemic Network Analysis: A Case for Unplugged Computational Thinking. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Peel, Amanda – PersonEntity: Name: NameFull: Arastoopour Irgens, Golnaz IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10590145 Numbering: – Type: volume Value: 34 – Type: issue Value: 5 Titles: – TitleFull: Journal of Science Education & Technology Type: main |
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