Weerinphas Chimnam, Dussadee Praserttitipong, and Pruet Boonma
Published in Data Science and Engineering (DSE) Record 2026 Vol. 7 No. 1 pp. 99-118
Abstract
This research investigates grade prediction and recommendation for selectable course options under sparse educational data conditions. In this study, elective courses are interpreted as courses for which students have enrollment choices, including Major Elective, Free Elective, and General Education courses where applicable. To address these challenges, the study proposes an ensemble collaborative filtering technique for an elective courses recommender system. The proposed technique integrates collaborative filtering with feature-based regression models and combines historical academic performance, course metadata, and semantic similarity from course descriptions to improve prediction accuracy and coverage. A time-aware evaluation protocol is applied to simulate realistic academic progression and prevent temporal data leakage. Experimental results show that the proposed ensemble models outperform single-model approaches, especially for near-cold-start users, while also maintaining prediction capability for new-item cases. The findings demonstrate that the proposed technique balances accuracy, robustness, and coverage. The system can serve as an additional tool to help students consider selectable course options rather than as a definitive course-selection mechanism.