International Journal of Artificial Intelligence and Education Technology
2835-2432ISSN (Online)
Volume 4 , Issue 1 , PP: 08-22, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Aa Hubur 1 * , Aygul Z. Ibatova 2
Student retention in higher education institutions is a critical problem that causes academic and financial challenges to individual students and to schools and entire countries. The field of study should be in the area of student retention as it enables educational facilities to provide appropriate intervention. The present study implements a comparative analysis of five machine learning classifiers; Linear Discriminant Analysis, K-Nearest Neighbours, Support Vector Machine, Random Forest and Gradient Boosting classifiers on dataof 4424 students who were selected from the Realinho et al. (2022) data set which contains demographic and socioeconomic, and macroeconomic and academic performance data from a Portuguese higher education institution over a decade. The mutual information feature selection step reduces the 22-dimensional feature space prior to model trainingby selecting 12 features that have, statistically, the highest discriminative power. Five-fold stratified cross-validation shows that the best overall performance is achieved by a SVM with a radial basis function kernel with accuracy of 97.1% and F1 score of 0.954 and all five models achieve AUC greater than 0.981. The importance analysis reveals that the combination of four measures of academic success from the first two semesters constructs 87.6% of the signal that Random Forest model uses for prediction which is driven by the most important predictor - number of curricular units that the student passes during the secondsemester (importance= 0.335). The impact of all socioeconomic and demographic and macroeconomic factors is less than 13%. The findings of the study have three implications about risk factors in student retention via empirical measurement.
Student dropout prediction , Machine learning , Educational data mining , Mutual information feature selection , Higher education analytics , Support vector machine , Random Forest , Early warning systems
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