IJBTT

Prediction and Classification Model for Academic Performance in Higher Institutions using Fuzzy Logic
	© 2022 by IJCTT Journal
	Volume-70 Issue-10
	Year of Publication : 2022
	Authors : Ogunlere Samson, Maitanmi Stephen, Kanu Richmond, Somefun Olawale
	DOI :  10.14445/22312803/IJCTT-V70I10P103

How to Cite?

Ogunlere Samson, Maitanmi Stephen, Kanu Richmond, Somefun Olawale, "Prediction and Classification Model for Academic Performance in Higher Institutions using Fuzzy Logic ," International Journal of Computer Trends and Technology, vol. 70, no. 10, pp. 13-21, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I10P103

Abstract
Predicting student academic performance plays an important role in students` achievement and evaluations of academic programs. It is evident that the traditional methods of selecting students for higher institutions of learning in Nigeria have not yielded positive results, especially those on scholarships. Classifying students using traditional techniques cannot give the desired level of accuracy and may result in a waste of resources and truncation of the continuity of such generosity. Challenges deem to have caused this include the method of students` selection. However, automating this technique will be more beneficial to all parties. This research proposes a Fuzzy Logic model that will predict and classify students` performance in higher institutions according to their merit by considering some factors. The factors considered in building this model are the Ordinary (O` level), Joint Admission and Matriculation Board (JAMB) exam results, and the applicant`s age. A Scikit Learn Neural Network package and a Multi-Layer Perception (MLP) classifier are chosen for the algorithm used for the implementation, while Fuzzy Logic is used as the concept. The model analysis shows that the traditional system is not the best logical approach. Rather, the use of fuzzy logic reduces the complexity and enhances the performance of selection processes while, at the same time, ensuring the best applicants get selected on merit. A good prediction of a student`s success is one way to be in an education system of competition; hence the use of computing methodology is justified for its real-time applicability.

Keywords
Prediction, Academic performance, Fuzzy logic, Scikit learn neural network.

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