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

Main Article Content

Ryosuke Nakamoto
Brendan Flanagan
Yiling Dai
Taisei Yamauchi
Kyosuke Takami
Hiroaki Ogata

Abstract

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.

Metrics

Metrics Loading ...

Article Details

How to Cite
Nakamoto, R., Flanagan, B., Dai, Y., Yamauchi, T., Takami, K., & Ogata, H. (2025). Integrating self-explanation and operational data for impasse detection in mathematical learning. Research and Practice in Technology Enhanced Learning, 20, 019. https://doi.org/10.58459/rptel.2025.20019
Section
Articles

Most read articles by the same author(s)

<< < 2 > >>