{"title":"Integrating self-explanation and operational data for impasse detection in mathematical learning","authors":"Ryosuke Nakamoto, B. Flanagan, Yiling Dai, Taisei Yamauchi, Kyosuke Takami, Hiroaki Ogata","doi":"10.58459/rptel.2025.20019","DOIUrl":null,"url":null,"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.","PeriodicalId":37055,"journal":{"name":"Research and Practice in Technology Enhanced Learning","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research and Practice in Technology Enhanced Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.58459/rptel.2025.20019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
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.