Beyond Average Effects in Education Research Syntheses: Explaining Heterogeneity of Concept Mapping Research in Science Education through Meta-Regression Modeling
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| Title: | Beyond Average Effects in Education Research Syntheses: Explaining Heterogeneity of Concept Mapping Research in Science Education through Meta-Regression Modeling |
|---|---|
| Language: | English |
| Authors: | Aaron Christian Wenger |
| Source: | ProQuest LLC. 2024Ph.D. Dissertation, Western Michigan University. |
| Availability: | ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml |
| Peer Reviewed: | N |
| Page Count: | 224 |
| Publication Date: | 2024 |
| Document Type: | Dissertations/Theses - Doctoral Dissertations |
| Descriptors: | Science Education, Educational Research, Concept Mapping, Teaching Methods, Learning Activities, Instructional Effectiveness, Active Learning, Formative Evaluation, Group Discussion, Electronic Learning, Directed Reading Activity, Cooperative Learning, Barriers |
| ISSN: | 3468-8900 |
| ISBN: | 979-83-468-8900-7 |
| Abstract: | Contemporary education research seeks causal inferences in order to guide policy and practice. Often well-controlled quasi-experimental and experimental designs are used as they are very useful for supporting and contextualizing causal inferences. Nevertheless, individual studies, even high-quality, resource-intensive randomized controlled trials, are not able to establish generalized claims. Because primary studies vary over dozens of variables (e.g., grade, subject, duration) simply conducting additional primary studies cannot change this. Furthermore, the informal assessments of primary studies common in literature reviews are unable to resolve certain challenges (e.g., publication bias) that are described in the emerging field of meta-research. These challenges require systematic methods, as well as expert knowledge, to assess generalized claims and accumulate knowledge. In this three-paper dissertation, systematic review and meta-analyses methods are applied to the case of concept mapping instructional interventions in science education. Concept mapping (CM) is a pedagogical tool which was first innovated in the 1970s and has been widely regarded as an effective learning activity. A concept map consists of conceptual nodes with connecting verbal links; each node-link-node connection forms a proposition. CM has been applied in a variety of ways and for a variety of instructional purposes such as collaborative learning, group discussion, directed reading, and formative assessment. With the move of classes to online settings during the COVID-19 pandemic, CM has received even more attention in science education as a tool for active learning in virtual settings (Gerber Hornink and Costa 2021; Choe 2020). Several meta-analyses have already been performed assessing the efficacy of CM interventions and have found significant positive effects. However, these meta-analyses have significant methodological limitations and focus on mean effects rather than on heterogeneity. Also, CM has had less impact on the educational profession than some of its advocates would have hoped (e.g., Kinchin, 2001). For these reasons, CM was deemed a good topic to investigate, serving as a case study of instructional interventions in science education. The first paper discusses the challenges to knowledge accumulation in education research and reviews how research synthesis methods may be applied to address these challenges. This informs and motivates the second two papers which each serve as case studies of meta-regression used to explore heterogeneity in science education research. The second paper is a systematic review and meta-analysis of CM interventions in biology education which applies recent innovations in meta-regression modeling and informs the last paper. The last paper is another systematic review and meta-analysis of CM interventions which expands the scope to all science education research, implements an improved systematic review process, and applies machine learning to generate the best possible meta-regression model for explaining heterogeneity. From the results of these two meta-analyses, it is argued that there is little evidence supporting the use of CM interventions in science education and that primary studies should change certain methodological practices. Science educators and policy makers should read the science education research critically and should not accept generalized claims without sufficient evidence. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Access URL: | https://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:31636177 |
| Accession Number: | ED670739 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: ED670739 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 0 |
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Often well-controlled quasi-experimental and experimental designs are used as they are very useful for supporting and contextualizing causal inferences. Nevertheless, individual studies, even high-quality, resource-intensive randomized controlled trials, are not able to establish generalized claims. Because primary studies vary over dozens of variables (e.g., grade, subject, duration) simply conducting additional primary studies cannot change this. Furthermore, the informal assessments of primary studies common in literature reviews are unable to resolve certain challenges (e.g., publication bias) that are described in the emerging field of meta-research. These challenges require systematic methods, as well as expert knowledge, to assess generalized claims and accumulate knowledge. In this three-paper dissertation, systematic review and meta-analyses methods are applied to the case of concept mapping instructional interventions in science education. Concept mapping (CM) is a pedagogical tool which was first innovated in the 1970s and has been widely regarded as an effective learning activity. A concept map consists of conceptual nodes with connecting verbal links; each node-link-node connection forms a proposition. CM has been applied in a variety of ways and for a variety of instructional purposes such as collaborative learning, group discussion, directed reading, and formative assessment. With the move of classes to online settings during the COVID-19 pandemic, CM has received even more attention in science education as a tool for active learning in virtual settings (Gerber Hornink and Costa 2021; Choe 2020). Several meta-analyses have already been performed assessing the efficacy of CM interventions and have found significant positive effects. However, these meta-analyses have significant methodological limitations and focus on mean effects rather than on heterogeneity. Also, CM has had less impact on the educational profession than some of its advocates would have hoped (e.g., Kinchin, 2001). For these reasons, CM was deemed a good topic to investigate, serving as a case study of instructional interventions in science education. The first paper discusses the challenges to knowledge accumulation in education research and reviews how research synthesis methods may be applied to address these challenges. This informs and motivates the second two papers which each serve as case studies of meta-regression used to explore heterogeneity in science education research. The second paper is a systematic review and meta-analysis of CM interventions in biology education which applies recent innovations in meta-regression modeling and informs the last paper. The last paper is another systematic review and meta-analysis of CM interventions which expands the scope to all science education research, implements an improved systematic review process, and applies machine learning to generate the best possible meta-regression model for explaining heterogeneity. From the results of these two meta-analyses, it is argued that there is little evidence supporting the use of CM interventions in science education and that primary studies should change certain methodological practices. Science educators and policy makers should read the science education research critically and should not accept generalized claims without sufficient evidence. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:31636177" linkWindow="_blank">https://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:31636177</link> – Name: AN Label: Accession Number Group: ID Data: ED670739 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 224 Subjects: – SubjectFull: Science Education Type: general – SubjectFull: Educational Research Type: general – SubjectFull: Concept Mapping Type: general – SubjectFull: Teaching Methods Type: general – SubjectFull: Learning Activities Type: general – SubjectFull: Instructional Effectiveness Type: general – SubjectFull: Active Learning Type: general – SubjectFull: Formative Evaluation Type: general – SubjectFull: Group Discussion Type: general – SubjectFull: Electronic Learning Type: general – SubjectFull: Directed Reading Activity Type: general – SubjectFull: Cooperative Learning Type: general – SubjectFull: Barriers Type: general Titles: – TitleFull: Beyond Average Effects in Education Research Syntheses: Explaining Heterogeneity of Concept Mapping Research in Science Education through Meta-Regression Modeling Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aaron Christian Wenger IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: isbn-print Value: 979-83-468-8900-7 – Type: issn-print Value: 3468-8900 Titles: – TitleFull: ProQuest LLC Type: main |
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