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

• Published in: Sustainability 2023-11, Vol.15 (21), p.15577
• Main Authors: Nakamoto, Ryosuke, Flanagan, Brendan, Dai, Yiling, Yamauchi, Taisei, Takami, Kyosuke, Ogata, Hiroaki
• Format: Article
• Language: English
• Subjects: Analysis; Cognition & reasoning; Collaboration; Customization; Junior high school students; Knowledge; Reading comprehension
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su152115577

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.