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
- Covenant Polytechnic, Aba, Abia State 1
- Covenant University, Canaan Land, Ota, Ogun State 4
- Crawford University of Apostolic Faith Mission Faith City, Igbesa, Ogun State 2
- Crescent University, Abeokuta, Ogun State 1
- Cross Rivers University of Technology, Calabar, Cross Rivers State 142
- Delta State Polytechnic, Ogwashi-Uku, Delta State 11
- Delta State Polytechnic, Otefe, Delta State 12
- Delta State University, Abraka, Delta State 139
- Ebonyi State University, Abakaliki, Ebonyi State 17
- Edo University, Iyamho, Edo State 10