An Automated Impasse Detection System Based on the Analysis of Self-Explanations in Mathematics - Nakamoto, Ryosuke; Flanagan, Brendan; Dai, Yiling; Takami, Kyosuke; Ogata, Hiroaki (Proceedings (IEEE International Conference on Advanced Learning Technologies), 2024)
In online mathematics education, self-explanation is increasingly recognized as a key tool for improving learning outcomes. Identifying learning impasses, which present significant educational challenges, is crucial. Typically, detecting these impasses demands considerable effort from educators to manually review and identify issues in students' mathematical reasoning.This paper introduces a fully automated impasse detection system designed for online math learning that leverages self-explanations. The system collects high-quality data from students working on the same quizzes, generates example answers, and uses these as benchmarks to identify where students are struggling. The system architecture is described in detail, focusing on the methods used to gather and validate high-quality self-explanation data.Empirical analysis using text regression models shows promising results: the models predict self-explanation scores with an error rate of 0.585 for validation data and 0.655 for evaluation data. While there are variations in scoring accuracy across different mathematical topics, the findings suggest that the system has the potential to significantly improve mathematics education by automating the detection of learning impasses.