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Enhancing Self-Explanation Learning through a Real-Time Feedback System: An Empirical Evaluation Study

Author

Listed:
  • Ryosuke Nakamoto

    (Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan)

  • Brendan Flanagan

    (Center for Innovative Research and Education in Data Science, Institute for Liberal Arts and Sciences, Kyoto University, Kyoto 606-8316, Japan)

  • Yiling Dai

    (Academic Center for Computing and Media Studies, Kyoto University, Kyoto 606-8317, Japan)

  • Taisei Yamauchi

    (Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan)

  • Kyosuke Takami

    (Academic Center for Computing and Media Studies, Kyoto University, Kyoto 606-8317, Japan
    National Institute for Educational Policy Research (NIER), Tokyo 100-8951, Japan)

  • Hiroaki Ogata

    (Academic Center for Computing and Media Studies, Kyoto University, Kyoto 606-8317, Japan)

Abstract

This research introduces the self-explanation-based automated feedback (SEAF) system, aimed at alleviating the teaching burden through real-time, automated feedback while aligning with SDG 4’s sustainability goals for quality education. The system specifically targets the enhancement of self-explanation, a proven but challenging cognitive strategy that bolsters both conceptual and procedural knowledge. Utilizing a triad of core feedback mechanisms—customized messages, quality assessments, and peer-generated exemplars—SEAF aims to fill the gap left by traditional and computer-aided self-explanation methods, which often require extensive preparation and may not provide effective scaffolding for all students. In a pilot study involving 50 junior high students, those with initially limited self-explanation skills showed significant improvement after using SEAF, achieving a moderate learning effect. A resounding 91.7% of participants acknowledged the system’s positive impact on their learning. SEAF’s automated capabilities serve dual purposes: they offer a more personalized and scalable approach to student learning while simultaneously reducing the educators’ workload related to feedback provision.

Suggested Citation

  • Ryosuke Nakamoto & Brendan Flanagan & Yiling Dai & Taisei Yamauchi & Kyosuke Takami & Hiroaki Ogata, 2023. "Enhancing Self-Explanation Learning through a Real-Time Feedback System: An Empirical Evaluation Study," Sustainability, MDPI, vol. 15(21), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15577-:d:1273270
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    References listed on IDEAS

    as
    1. Pei-Yao Su & Zi-Ying Zhao & Qi-Gan Shao & Pei-Yuan Lin & Zhe Li, 2023. "The Construction of an Evaluation Index System for Assistive Teaching Robots Aimed at Sustainable Learning," Sustainability, MDPI, vol. 15(17), pages 1-24, September.
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