Credit-based curricula require students to select courses every semester while respecting prerequisites, course offering schedules, and reasonable workload distribution. In practice, advising capacity is limited, and suboptimal registrations can increase time-to-degree. This study develops a study plan recommendation system for Saigon University by integrating three complementary signals: (i) Program- and Semester-Level Course Popularity, (ii) Profile-Based Similarity Using Student Attributes, and (iii) User-Based Collaborative Filtering on Historical Grade Patterns. The hybrid scoring function applies to stage-dependent weights to reflect differences in informational availability and decision needs across years of study. The system is trained and evaluated in institutional data from three programs (Information Technology, Office Administration, and Psychology), including 1,847 on-time graduates and 156,995 grade records. Offline evaluation with 5-fold cross-validation shows that the hybrid method achieves Precision@10 of 82.3%, Recall@10 of 78.6%, and F1@10 of 80.4%, outperforming individual baselines. An online pilot with 245 students reports 87.3% overall satisfaction and an average usability score of 4.2/5. The results suggest that combining complementary signals can provide actionable, context-aware study plan recommendations in a real university setting.
Research Article | Open Access | Download Full Text
Volume 5 | Issue 1 | Year 2026 | Article Id: DST-V5I1P102 DOI: https://doi.org/10.59232/DST-V5I1P102
Developing a Study Plan Recommendation System for Students at Saigon University
Thanh Minh Cao, Loan Ngoc Nhu Do, Hieu Nguyen Minh Tran
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 26 Nov 2025 | 28 Dec 2025 | 20 Jan 2026 | 30 Jan 2026 |
Citation
Thanh Minh Cao, Loan Ngoc Nhu Do, Hieu Nguyen Minh Tran. “Developing a Study Plan Recommendation System for Students at Saigon University.” DS Journal of Digital Science and Technology, vol. 5, no. 1, pp. 14-22, 2026.
Abstract
Keywords
Collaborative Filtering, Content-Based Filtering, Hybrid Recommender System, Study Plan Recommendation, Academic Advising, Higher Education.
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