Explainable Bayesian Network Recommender for Personalized University Program Selection - Philippe Boribo Kikunda; Jérémie Ndikumagenge; Longin Ndayisaba; Thierry Nsabimana (Journal of Computing Theories and Applications, 2025)
In a context where students face increasingly complex academic choices, this work proposes a recommendation system based on Bayesian networks to guide new baccalaureate holders in their university choices. Using a dataset containing variables such as secondary school section, gender, type of school, percentage obtained, age, and first-year honors, we have constructed a probabilistic model capturing the dependencies between these characteristics and the option chosen. The data is collected at the Catholic University of Bukavu, the Official University of Bukavu, and the Higher Institute of Education of Bukavu, preprocessed and then used to learn the structure via the hill-climbing algorithm with the BIC score using R's bnlearn tool. The model enables us to estimate the probability that a candidate will choose a given stream, depending on their profile. The approach has been validated using metrics such as BIC, cross-validation, and bootstrap and offers a good compromise between interpretability and predictive performance. The results highlight the potential of Bayesian networks in constructing explainable recommendation systems in the field of academic guidance. The system produces orientation probability maps for each candidate, which can be used by enrollment service advisers, as well as an ordered list of options relevant to the candidate's profile. With a remarkable performance on a test sample of precision@k=0.85, recall@k=0.61, ndcg=0.8, and Map=0.88, it constitutes an effective lever for reducing the risk of being misdirected in universities in South-Kivu, in the Democratic Republic of Congo