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

Ryosuke Nakamoto (), Brendan Flanagan (), Yiling Dai, Taisei Yamauchi, Kyosuke Takami and Hiroaki Ogata
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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
Hiroaki Ogata: Academic Center for Computing and Media Studies, Kyoto University, Kyoto 606-8317, Japan

Sustainability, 2023, vol. 15, issue 21, 1-22

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.

Keywords: self-explanation; automatic feedback system; real-time feedback; classmates’ self-explanations reference; natural language processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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