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
- Federal University of Technology, Minna, Niger State 47
- Federal University of Technology, Owerri, Imo State 98
- Federal University Oye-Ekiti, Ekiti State 47
- Federal University, Birnin-Kebbi, Kebbi State 42
- Federal University, Dutse, Jigawa State 9
- Federal University, Dutsin-Ma, Katsina State 65
- Federal University, Gashua, Yobe State 3
- Federal University, Gusau, Zamfara State 14
- Federal University, Kashere, Gombe State 1
- Federal University, Lafia, Nasarawa State 6