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Ndayisaba, Longin
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Journal of Computing Theories and Applications

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Explainable Bayesian Network Recommender for Personalized University Program Selection Kikunda, Philippe Boribo; Ndikumagenge, Jérémie; Ndayisaba, Longin; Nsabimana, Thierry
Journal of Computing Theories and Applications Vol. 3 No. 1 (2025): JCTA 3(1) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12720

Abstract

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
Predicting First-Year Student Performance with SMOTE-Enhanced Stacking Ensemble and Association Rule Mining for University Success Profiling Kikunda, Philippe Boribo; Kasongo, Issa Tasho; Nsabimana, Thierry; Ndikumagenge, Jérémie; Ndayisaba, Longin; Mushengezi, Elie Zihindula; Kala, Jules Raymond
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): in progress
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14043

Abstract

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.
Co-Authors Kala, Jules Raymond Kasongo, Issa Tasho Kikunda, Philippe Boribo Mushengezi, Elie Zihindula Ndikumagenge, Jeremie Nsabimana, Thierry