Comparing supervised learning models for classifying learned helplessness in a mathematics tutoring environment

Abstract

Learned helplessness affects student motivation and performance, especially in mathematics, where repeated difficulty often leads students to withdraw from tasks. Most existing research has used surveys and teacher observations to study this phenomenon. While these methods provide insights, they do not capture behavioral data or support real-time identification. There is a need for approaches that use behavioral data from learning technologies and apply interpretable machine learning to detect signs of helplessness. This study used interaction logs and academic profiles from 113 Grade 8 students in five public schools in the Philippines. The students used a web-based mathematics tutoring application for 30 minutes under self-paced conditions. The study compared six supervised learning models to classify students as either high or low in learned helplessness based on teacher ratings. Results indicated that Explainable Boosting Machine (EBM), XGBoost, CatBoost, LightGBM, and Random Forest showed high performance, and EBM produced a slightly higher test balanced accuracy. EBM also provided the most interpretable results by identifying general weighted average, mathematics anxiety, time spent, and problem-solving success as the most influential predictors. These results show that learned helplessness can be classified using academic and behavioral indicators collected in digital learning environments. Interpretable models such as EBM can support early detection and guide adaptive instruction. Future studies should examine how models like EBM can help teachers respond to students’ needs across a wider range of subjects and settings.