Predicting Students’ Academic Performance Using Regression Analysis

Rolly T. Dagdagui^1,

¹Saint Mary’s University, Nueva Viscaya, Philippines

Cite this paper

Rolly T. Dagdagui. Predicting Students’ Academic Performance Using Regression Analysis. American Journal of Educational Research. 2022; 10(11):640-646. doi: 10.12691/EDUCATION-10-11-2

Abstract

This study generally aimed to develop a predictive model for students' academic performance. Correlational research designs were employed in this study to explore the relationships between the students' sex, course, senior high school data, admission ability test, and first-year performance of the students. A statistical model for predicting first-year students' academic performance based on admission data and first-year academic performance was formulated. The study was conducted in one of the State Universities and Colleges (SUCs) in Cordillera Administrative Region (CAR). The population of the study was 521 first-year students enrolled during the first semester and completed up to the second semester of the school year 2019-2020. From the findings, the academic performance of the students is highly associated with their high school GWA, strand, admission ability, course, and sex. The academic performance of first-year students can be predicted using Multiple Linear Regression (MLR) analysis, a model that considers the high school GWA, strand, and admission general ability, of the students as significant predictors. The derived model has a predictive power of around 67.30% and the model was found a good model. It is then suggested that the highly associated data to students' academic performance like high school GWA and admission test results can be considered in the recruitment and admission of students, especially, in the programs with board examinations. The same predictors are suggested to be accommodated in developing programs and interventions which could improve the student's academic performance. Meanwhile, the administrators are encouraged to consider the use of the models to track the students and provide necessary interventions to those who are likely to perform poorly in their courses. The model can also be considered in crafting amendments to the admission and retention policy of the said college.

Keywords

students’ academic performance, enrolment data, predictors, predictive model, multiple linear regression analysis

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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