Integrating self-explanation and operational data for impasse detection in mathematical learning

印为红字的 笔迹 计算机科学 人工智能 钥匙(锁) 特征(语言学) 机器学习 数学教育 心理学 计算机安全 语言学 哲学
作者
Ryosuke Nakamoto,Brendan Flanagan,Yiling Dai,Taisei Yamauchi,Kyosuke Takami,Hiroaki Ogata
出处
期刊:Research and Practice in Technology Enhanced Learning [Springer Nature]
卷期号:20: 019-019
标识
DOI:10.58459/rptel.2025.20019
摘要

Self-explanation is increasingly recognized as a key factor in learning. Identifying learning impasses, which are significant educational challenges, is also crucial as they can lead to deeper learning experiences. This paper argues that integrating self-explanation with relevant datasets is essential for detecting learning impasses in online mathematics education. To test this idea, we created an evaluative framework using a rubric-based approach tailored for mathematical problem-solving. Our analysis combines various data types, including handwritten responses and digital self-explanations from 93 middle school students. Using hierarchical logistic regression, we examined feature groups such as Self-Explanation Quality, Handwriting Features, and Overall Level of Action. Models based solely on self-explanation achieved a 74.0% accuracy rate, while adding more features increased the final model’s accuracy to 80.06%. This improvement highlights the effectiveness of an integrated approach. The combined model, which merges generated handwriting features counts with self-explanation features, shows the importance of both qualitative and quantitative measures in identifying learning impasses. Our findings suggest that a comprehensive approach, leveraging detailed operational data and rich self-explanation content, can enhance the detection of learning challenges, providing insights for personalized education in online learning environments.

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