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Menampilkan 1–2 dari 2 artikel
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)
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 outnu...
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Explainable Bayesian Network Recommender for Personalized University Program Selection
Journal of Computing Theories and Applications
Vol 3
, No 1
(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...
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