Predicting Academic Performance of Low-Income Students in Public Ecuadorian Online Universities: An Educational Data Mining Approach

Jorge Rodas-Silva, Jorge Parraga-Alava

2023

Abstract

The success of higher education institutions in the online learning environment can be measured by the performance of students. Identifying backgrounds or factors that increase the academic success rate of online students is especially helpful for educational decision-makers to adequately plan actions to promote successful outcomes in this digital landscape. In this paper, we identify the factors that contribute to the academic success of students in public Ecuadorian online universities and develop a predictive model to aid in improving their performance. Our approach involved five stages: data collection and description, which involved gathering data from universities, including social demographic, and academic features. In preprocessing, cleaning, and transforming the data to prepare it for analysis was performed. Modeling involved applying machine learning algorithms to identify patterns and key factors to predict student outcomes. It was validated in the next stage where the performance of feature selection and predictive model was tackled. In the last stage, were interpreted the results of the analysis about the factors that contribute to the academic success of low-income students in online universities in Ecuador. The results suggest that the grade in the leveling course, the family income, and the age of the student mainly influence their academic performance. The best performances were achieved with Boruta + Random Forest and LVQ + SVM, reaching an accuracy of 75.24% and 68.63% for binary (Pass/Fail) and multiclass (Average/Good/Excellent) academic performance prediction, respectively.

Paper Citation

in Harvard Style

Rodas-Silva J. and Parraga-Alava J. (2023). Predicting Academic Performance of Low-Income Students in Public Ecuadorian Online Universities: An Educational Data Mining Approach. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 52-63. DOI: 10.5220/0012086300003541

in Bibtex Style

@conference{data23,
author={Jorge Rodas-Silva and Jorge Parraga-Alava},
title={Predicting Academic Performance of Low-Income Students in Public Ecuadorian Online Universities: An Educational Data Mining Approach},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={52-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012086300003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Predicting Academic Performance of Low-Income Students in Public Ecuadorian Online Universities: An Educational Data Mining Approach
SN - 978-989-758-664-4
AU - Rodas-Silva J.
AU - Parraga-Alava J.
PY - 2023
SP - 52
EP - 63
DO - 10.5220/0012086300003541
PB - SciTePress