Predicting Academic Success of College Students Using Machine Learning Techniques
Metadata
Show full item recordEditorial
Guanin Fajardo, J et. al. 2024, 9, 60. [https://doi.org/10.3390/data9040060]
Materia
educational data mining machine learning educational analysis
Date
2024-04-22Sponsorship
project “Factors that influence the completion of the time to degree and affect the retention of students at UTEQ”; UTEQ-FOCICYT IX-2023/29 projectAbstract
College context and academic performance are important determinants of academic success;
using students’ prior experience with machine learning techniques to predict academic success
before the end of the first year reinforces college self-efficacy. Dropout prediction is related to
student retention and has been studied extensively in recent work; however, there is little literature
on predicting academic success using educational machine learning. For this reason, CRISP-DM
methodology was applied to extract relevant knowledge and features from the data. The dataset
examined consists of 6690 records and 21 variables with academic and socioeconomic information.
Preprocessing techniques and classification algorithms were analyzed. The area under the curve was
used to measure the effectiveness of the algorithm; XGBoost had an AUC = 87.75% and correctly
classified eight out of ten cases, while the decision tree improved interpretation with ten rules in
seven out of ten cases. Recognizing the gaps in the study and that on-time completion of college
consolidates college self-efficacy, creating intervention and support strategies to retain students is a
priority for decision makers. Assessing the fairness and discrimination of the algorithms was the
main limitation of this work. In the future, we intend to apply the extracted knowledge and learn
about its influence of on university management.