doi: 10.18178/ijiet.2023.13.6.1888
Prediction of Senior High School Students’ Performance in a State University: An Educational Data Mining Approach
Jonardo R. Asor1,*
, , , ,
- 1College of Computer Studies, Laguna State Polytechnic University, Los Baños Campus, Los Baños, Laguna, Philippines
- 2College of Arts and Sciences, Laguna State Polytechnic University, Los Baños Campus, Los Baños, Laguna, Philippines
- 3College of Teacher Education, Laguna State Polytechnic University, Los Baños Campus, Los Baños, Laguna, Philippines
- Manuscript receivedAugust 28, 2022
- revisedSeptember 21, 2022
- acceptedNovember 25, 2022
Abstract
The study was conducted to predict the performance of the senior high school students in one state university using machine learning algorithms. Data mining process was followed to develop a model for predicting the students’ performance. The 4-year records of senior high school students of Laguna State Polytechnic University-Los Baños was gathered and used in the model development through well-known machine learning algorithms such as decision tree, naïve bayes, random forest, neural network and linear regression. Upon the development of the models, it is found in this study that naïve bayes performs well against the remaining algorithms and neural networks also shown a promising result in predicting student performance. The study also found that senior high school students have a high chance of not performing well upon entering the school based on the prediction of naïve bayes showing a high probability of satisfactory rating in Grade 11-1st semester applied subjects. Moreover, among the strands offered in Laguna State Polytechnic University-Los Baños, Accountancy, Business and Management students predicted to have the highest chance of having outstanding performance while Information and Communications Technology students predicted to have a high chance of satisfactory.
Keywords
- Machine learning
- naïve bayes
- neural network
- decision tree
- random forest
- linear regression
How to Cite
Jonardo R. Asor, Gene Marck B. Catedrilla, Chester Alexis C. Buama, Monique E. Malabayabas, and Cristina E. Malabayabas, "Prediction of Senior High School Students’ Performance in a State University: An Educational Data Mining Approach," International Journal of Information and Education Technology, vol. 13, no. 6, pp. 925-931, 2023. https://doi.org/10.18178/ijiet.2023.13.6.1888
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).