Educational assessment requires understanding student problem-solving processes, not just final answers. Current AI-driven analytics focus on static outcomes, missing valuable insights from temporal dynamics. Explain-from-Stroke is a practical framework that captures invisible learning processes by integrating handwriting dynamics with vision-language models. The system extracts temporal features such as writing speed, pauses, and revisions, providing additional context for generating meaningful insights into hidden aspects of student reasoning. Using real classroom data from a Japanese secondary school, the model shows an 18.2% improvement in cognitive depth analysis compared with static approaches. This work provides educators with an accessible method to analyze learning processes using standard tablet technology.

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.

In the field of mathematics education, self-explanation is recognized as a critical facilitator for learners to articulate their understanding of complex mathematical concepts and problem-solving techniques. With the emergence of digital learning platforms, the potential to utilize such self-explanations for automated evaluation has expanded, yet significant challenges remain. This study introduces a method that integrates vision and language models to enhance the accuracy of automated evaluations of self-explanations in mathematics quizzes. By leveraging the CLIP encoder, we utilize features from both handwritten images and textual self-explanations, aiming to incorporate the characteristics of handwritten solutions that have been overlooked by text-only evaluations. Models were developed to include self-explanations alone (baseline) and those that integrate image features, using both the original and a fine-tuned CLIP encoder adapted to our dataset of self-explanations and handwritten images. Experimental results demonstrated that the model utilizing the fine-tuned CLIP significantly outperformed the baseline, showing a notable reduction in MAE. Conversely, the model employing the original CLIP encoder exhibited decreased performance compared to the baseline, revealing the complex interplay between integrating self-explanations and image features. These findings suggest that the benefits of embedding image features depend on the quality and appropriateness of the visual data incorporated.