Development and Integration of a Classification Model for Student Programming Anxiety Levels

Abstract

Reducing student anxiety is a critical factor in enhancing academic performance, with timely identification of mental health concerns serving as a key component of effective educational support strategies. This study presents the development and integration of a classification model designed to detect students’ current programming anxiety levels based on academic, demographic, and behavioral attributes. The model was trained and evaluated using student data collected from enrolled in computing-related programs, and its performance was benchmarked against Support Vector Machine, Logistic Regression, Random Forest, J48 Decision Tree, and Naive Bayes classifiers. The Support Vector Machine achieved excellent performance, with an F-measure of 95.97%, an accuracy of 97.11%, precision of 94.20%, recall of 97.86%, and a Cohen’s kappa of 0.937, indicating strong agreement between predicted and actual classifications. Error-based metrics further supported the model’s reliability, with a Mean Absolute Error (MAE) of 0.0289, Root Mean Squared Error (RMSE) of 0.1641, Relative Absolute Error (RAE) of 0.04%, and Root Relative Squared Error (RRSE) of 34.40%. To enhance practical utility, the model was integrated into a web-based student information system that generates real-time classifications and visualizations, supporting educators in recognizing students who may require additional support. While the model provides valuable insights for identifying students exhibiting current programming anxiety, future work incorporating longitudinal data is needed to enable true predictive capabilities for early intervention.