What Course Should I Enroll In Next? Guiding Students Toward Academic Success with Sequential Recommendations
Gabriele Tolomei, Lorenzo Antonelli, Enrico Bassetti, Emanuele Panizzi · ACM Transactions on Recommender Systems · 2026
Personalized academic support plays a critical role in improving student engagement, reducing dropout rates, and enhancing the institutional reputation of universities. Although tutoring and mentoring services are available, students still lack access to an effective and timely course recommender system to guide their academic planning. This study aims to address that need by proposing a sequential recommender system trained on historical academic records from a prominent European university’s student portal. We develop and evaluate various state-of-the-art sequential recommendation models and enhance them by integrating exam grades as explicit feedback. Additionally, we introduce two re-ranking strategies that ( i ) increase recommendation diversity and ( ii ) amplify the influence of successful academic trajectories. Results from both offline and online experiments show that our best-performing method effectively anticipates relevant course choices, demonstrating the value of incorporating temporal patterns into the recommendation model.