Predicting University Student Performance Using Machine Learning Techniques
Student: Ayodeji Anthony Alese (Project, 2025)
Department of Computer and Information Science
Bamidele Olumilua University of Edu. Science and Tech. Ikere Ekiti, Ekiti State
Abstract
This research looks forward to developing a model with the ability to make effective analysis and prediction on the performance of students in regard to their advanced knowledge, using modern machine learning techniques. Through modern machine learning algorithms-supervised and unsupervised-finding insight into students' academic and behavioral data, the paper tends to be helpful for educational institutions as regards early intervention with better time-saving support for at-risk students by optimizing learning outcomes.
Keywords
For the full publication, please contact the author directly at: sanchezalese@gmail.com
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Institutions
- UMA UKPAI SCHOOL OF THEOLOGY, UYO, AKWA IBOM STATE (AFFL TO UNIVERSITY OF UYO) 1
- Umaru Ali Shinkafi Polytechnic, Sokoto, Sokoto State 24
- Umaru Musa Yaradua University, Katsina, Katsina State 28
- Umca, Ilorin (Affiliated To University of Ibadan), Kwara State 1
- University of Abuja, Abuja, Fct 117
- University of Africa, Toru-Orua, Bayelsa State 4
- University of Benin, Benin City, Edo State 362
- University of Calabar Teaching Hospital School of Health Information Mgt. 1
- University of Calabar, Calabar, Cross River State 240
- University of Ibadan, Ibadan, Oyo State 14