Review Article Open Access

A Systematic Review Regarding the Prediction of Academic Performance

Percy De-La-Cruz1, Rommel Rojas-Coaquira1, Hugo Vega-Huerta1, José Pérez-Quintanilla1 and Manuel Lagos-Barzola2
  • 1 Department of Computer Science, Universidad Nacional Mayor de San Marcos, Lima, Peru
  • 2 Department of Mathematics and Physics, San Cristóbal de Huamanga University, Ayacucho, Peru

Abstract

The prediction of students' academic performance is an area of great concern for universities and educational institutions since academic performance is one of the most important aspects of the learning process. To analyze this behavior, this study makes a critical analysis of the topic of interest and aims to review, analyze and summarize the latest research advances related to the prediction of academic performance. The systematic literature review method is applied to answer three questions: (1) What factors are determinants in predicting students' academic performance? (2) what methods are used to predict students' academic performance? and (3) what are the objectives and interests in predicting students' academic performance? After conducting the study of 50 outstanding articles, as results, we found that academic factor is the guideline for predicting academic performance; supervised machine learning is the most used technique, highlighting support vector machine, random forests, and neural networks; the most outstanding objectives for the application of prediction were: Student performance with 53%, risk of failure with 14%, search for student knowledge with 12%, avoid dropout with 12%, and decision making with 10%.

Journal of Computer Science
Volume 18 No. 12, 2022, 1219-1231

DOI: https://doi.org/10.3844/jcssp.2022.1219.1231

Submitted On: 23 August 2022 Published On: 23 December 2022

How to Cite: De-La-Cruz, P., Rojas-Coaquira, R., Vega-Huerta, H., Pérez-Quintanilla, J. & Lagos-Barzola, M. (2022). A Systematic Review Regarding the Prediction of Academic Performance. Journal of Computer Science, 18(12), 1219-1231. https://doi.org/10.3844/jcssp.2022.1219.1231

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Keywords

  • Academic Performance Prediction Academic Performance of Students
  • Machine Learning
  • Predictive Models
  • Systematic Review of Literature