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] |
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| Database: | Education Research Complete |
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