Predicting Student Graduation Using Multilayer Perceptron

S. K. Alaramma, B. I. Ya'u, A. A. Habu, M. Maishanu, M. K. Dauda, S. W. Mustapha, M. A. Musa

Abstract


The prediction of student graduation holds significant value for higher institutions as it addresses the issue of student dropout. While the number of graduating students typically exceeds that of dropouts in a given cohort, there has been a recent increase in the number of students discontinuing their university studies. Various factors, including marital status, age, and prior academic performance, contribute to this problem. University management and other stakeholders are concerned about this alarming situation and recognize the need for urgent action. To tackle this challenge, the researchers’ present a novel dataset comprising five key parameters: gender, age, results from ordinary level examinations (such as WAEC, NECO, or NABTEB), Post Unified Tertiary Matriculation Examination (PUTME) score, and first-semester Grade Point Average (GPA). We employ a Multi-Layer Perceptron (MLP) model to predict students' graduation at a university. The dataset encompasses 2,512 records collected over an 8-year period. For model training and testing, we utilized cross-validation with three k-fold variations, specifically 2, 5, and 8. The study reveals that the MLP model achieves an impressive accuracy of 97.3%, with a recall of 92.1% and precision of 94.7%. When compared to other state-of-the-art models, the MLP-based approach demonstrates superior accuracy. Predictive models like the one developed in this study serve as valuable tools for both students and management in addressing the dropout issue.


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