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

被引:7
作者
Nakamoto, Ryosuke [1 ]
Flanagan, Brendan [2 ]
Dai, Yiling [3 ]
Yamauchi, Taisei [1 ]
Takami, Kyosuke [3 ,4 ]
Ogata, Hiroaki [3 ]
机构
[1] Kyoto Univ, Grad Sch Informat, Kyoto 6068501, Japan
[2] Kyoto Univ, Inst Liberal Arts & Sci, Ctr Innovat Res & Educ Data Sci, Kyoto 6068316, Japan
[3] Kyoto Univ, Acad Ctr Comp & Media Studies, Kyoto 6068317, Japan
[4] Natl Inst Educ Policy Res NIER, Tokyo 1008951, Japan
关键词
self-explanation; automatic feedback system; real-time feedback; classmates' self-explanations reference; natural language processing; WORKED-EXAMPLES; MATHEMATICS; STUDENTS; PLATFORM; PROMPTS;
D O I
10.3390/su152115577
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
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页数:22
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