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A meta-analysis of the efficacy of self-regulated learning interventions on academic achievement in online and blended environments in K-12 and higher education

Author

Listed:
  • Zhihong Xu
  • Yingying Zhao
  • Bingsheng Zhang
  • Jeffrey Liew
  • Ashlynn Kogut

Abstract

Numerous empirical studies, including meta-analyses, have confirmed the impact of self-regulated learning (SRL) on learners’ academic achievement in traditional or face-to-face learning environments. However, prior meta-analyses rarely examined the efficacy of SRL interventions on academic achievement in online or blended education across elementary education, secondary education, higher education, and adult education. Therefore, this meta-analysis addresses this research gap by focusing on the effect of SRL interventions on students’ academic test performance in online and blended learning environments in elementary, secondary, and higher education settings as well as informal settings. The present meta-analysis compares SRL phase, SRL scaffolds, and SRL strategies between treatment and control groups. We also investigated possible differential effectiveness due to substantive features of the included studies, such as different educational levels of learners (e.g. elementary, secondary, and higher education), academic subjects (STEM vs. non-STEM), and learning contexts (e.g. online learning, blended learning, web-based learning, mobile learning). Consistent with previously published meta-analyses, the present meta-analysis confirmed a positive and moderate effect of SRL intervention (ES = 0.69) on learners’ academic achievement in online and blended environments for learners in elementary, secondary, and higher education as well as informal adult education settings.

Suggested Citation

  • Zhihong Xu & Yingying Zhao & Bingsheng Zhang & Jeffrey Liew & Ashlynn Kogut, 2023. "A meta-analysis of the efficacy of self-regulated learning interventions on academic achievement in online and blended environments in K-12 and higher education," Behaviour and Information Technology, Taylor & Francis Journals, vol. 42(16), pages 2911-2931, December.
  • Handle: RePEc:taf:tbitxx:v:42:y:2023:i:16:p:2911-2931
    DOI: 10.1080/0144929X.2022.2151935
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