Innovative Machine Learning Approach for Forecasting Student Performance in Degree Programs: A Case Study Using Naive Bayes Classification

Nana  Wang

doi:10.6180/jase.202603_29(3).0015

Innovative Machine Learning Approach for Forecasting Student Performance in Degree Programs: A Case Study Using Naive Bayes Classification

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Nana WangThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Liberal Education, Liaoning University of International Business and Economics, Dalian, 116052, China

Received: October 2, 2024
Accepted: April 19, 2025
Publication Date: July 11, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202603_29(3).0015

It has become one of the prime focus areas in the academic world, starting from schools to higher educational institutions, that deal with student academic performance predictions for the establishment of effective policies aimed at upraising academic excellence, among other issues such as dropouts. Some advantages of this project lie in the automation of different processes, usually associated with student activities by dealing with vast data arrays resulting from technologically enhanced learning software tools. Among these issues, there are significant variations of students, given their backgrounds and chosen courses; their even informativeness of different courses in a more accurate way, considering good predictions. The above-mentioned challenges are rudimentarily addressed in this paper by proposing a new machine-learning method designed to predict student performance for degree programs. This method contributes significantly to dealing with the difficulties presented by prediction and thus adds to knowledge in this area. Student performance is predicted using Naive Bayes Classification (NBC). In this study, to improve the models’ performances, two optimizers were utilized: the Population-Based Vortex (PSV) Search Algorithm and Electric Charged Particles Optimization (ECPO). Based on the obtained outcomes, the best model with a high accuracy rate is the NBPV model (NBC + PVS).

Keywords: Student Performance; model; Naive Bayes Classification; Population-Based Vortex Search Algorithm; Electric Charged Particles Optimization

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