Educational robots for student creativity development: Research trends and evidence from topic modeling, meta-analysis, and rule mining.
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| Title: | Educational robots for student creativity development: Research trends and evidence from topic modeling, meta-analysis, and rule mining. |
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| Authors: | Chen, Xieling1 xielingchen0708@gmail.com, Xie, Haoran2 hrxie2@gmail.com, Tao, Xiaohui3 xiaohui.tao@unisq.edu.au, Li, Lin4 cathylilin@whut.edu.cn, Peng, Xingquan1 2112408195@e.gzhu.edu.cn, Wang, Fu Lee5 pwang@hkmu.edu.hk |
| Source: | Educational Technology & Society. Jul2026, Vol. 29 Issue 3, p361-382. 22p. |
| Subject Terms: | *Creative teaching, *Evidence synthesis, *Teaching methods, *Computer programming education, *STEAM education, Robots, Association rule mining, Cluster analysis (Statistics) |
| Abstract: | With the increasing adoption of educational robots (ERs) to support creativity development, a comprehensive understanding of their research landscape, empirical effectiveness, and instructional conditions becomes necessary. This study addresses this need by proposing a three-stage analytical framework that involves topic modeling, meta-analysis, and rule mining. First, a BERTopic modeling approach was applied to identify four major research topics, including STEAM motivation and gamified project-based learning, creative coding and narrative play in early childhood, pedagogies for creative and computational thinking, and creative engineering and physical computing, with a growing emphasis on narrative and constructionist approaches, especially in early childhood education. Second, a Bayesian meta-analysis was utilized to identify a moderate overall effect size (Mean = 0.600), with subgroup analyses demonstrating that effect sizes varied significantly across experimental design factors such as subject domain, age group, learning duration, setting, instructional strategy, and measurement issue, where studies focusing on programming education, preschool and high school learners, and guided learning yielded the strongest effects. Finally, an associate rule mining approach was used to identify consistent patterns linking skill modeling, robot-as-tool roles, and mixed instructional strategies with positive creativity outcomes. Findings of this study contribute to both research and practice by enhancing researchers' and instructors' understanding of ER research trends and practical effectiveness in promoting creativity, with actionable instructional design recommendations being provided to optimize ER-based creativity interventions. [ABSTRACT FROM AUTHOR] |
| Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 195015212 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Educational robots for student creativity development: Research trends and evidence from topic modeling, meta-analysis, and rule mining. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Xieling%22">Chen, Xieling</searchLink><relatesTo>1</relatesTo><i> xielingchen0708@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Haoran%22">Xie, Haoran</searchLink><relatesTo>2</relatesTo><i> hrxie2@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Tao%2C+Xiaohui%22">Tao, Xiaohui</searchLink><relatesTo>3</relatesTo><i> xiaohui.tao@unisq.edu.au</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Lin%22">Li, Lin</searchLink><relatesTo>4</relatesTo><i> cathylilin@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Peng%2C+Xingquan%22">Peng, Xingquan</searchLink><relatesTo>1</relatesTo><i> 2112408195@e.gzhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Fu+Lee%22">Wang, Fu Lee</searchLink><relatesTo>5</relatesTo><i> pwang@hkmu.edu.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Educational+Technology+%26+Society%22">Educational Technology & Society</searchLink>. Jul2026, Vol. 29 Issue 3, p361-382. 22p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Creative+teaching%22">Creative teaching</searchLink><br />*<searchLink fieldCode="DE" term="%22Evidence+synthesis%22">Evidence synthesis</searchLink><br />*<searchLink fieldCode="DE" term="%22Teaching+methods%22">Teaching methods</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+programming+education%22">Computer programming education</searchLink><br />*<searchLink fieldCode="DE" term="%22STEAM+education%22">STEAM education</searchLink><br /><searchLink fieldCode="DE" term="%22Robots%22">Robots</searchLink><br /><searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the increasing adoption of educational robots (ERs) to support creativity development, a comprehensive understanding of their research landscape, empirical effectiveness, and instructional conditions becomes necessary. This study addresses this need by proposing a three-stage analytical framework that involves topic modeling, meta-analysis, and rule mining. First, a BERTopic modeling approach was applied to identify four major research topics, including STEAM motivation and gamified project-based learning, creative coding and narrative play in early childhood, pedagogies for creative and computational thinking, and creative engineering and physical computing, with a growing emphasis on narrative and constructionist approaches, especially in early childhood education. Second, a Bayesian meta-analysis was utilized to identify a moderate overall effect size (Mean = 0.600), with subgroup analyses demonstrating that effect sizes varied significantly across experimental design factors such as subject domain, age group, learning duration, setting, instructional strategy, and measurement issue, where studies focusing on programming education, preschool and high school learners, and guided learning yielded the strongest effects. Finally, an associate rule mining approach was used to identify consistent patterns linking skill modeling, robot-as-tool roles, and mixed instructional strategies with positive creativity outcomes. Findings of this study contribute to both research and practice by enhancing researchers' and instructors' understanding of ER research trends and practical effectiveness in promoting creativity, with actionable instructional design recommendations being provided to optimize ER-based creativity interventions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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=195015212 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.30191/ETS.202607_29(3).SP11 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 361 Subjects: – SubjectFull: Creative teaching Type: general – SubjectFull: Evidence synthesis Type: general – SubjectFull: Teaching methods Type: general – SubjectFull: Computer programming education Type: general – SubjectFull: STEAM education Type: general – SubjectFull: Robots Type: general – SubjectFull: Association rule mining Type: general – SubjectFull: Cluster analysis (Statistics) Type: general Titles: – TitleFull: Educational robots for student creativity development: Research trends and evidence from topic modeling, meta-analysis, and rule mining. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Xieling – PersonEntity: Name: NameFull: Xie, Haoran – PersonEntity: Name: NameFull: Tao, Xiaohui – PersonEntity: Name: NameFull: Li, Lin – PersonEntity: Name: NameFull: Peng, Xingquan – PersonEntity: Name: NameFull: Wang, Fu Lee IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11763647 Numbering: – Type: volume Value: 29 – Type: issue Value: 3 Titles: – TitleFull: Educational Technology & Society Type: main |
| ResultId | 1 |
