Classification Models for Determining Types of Academic Risk and Predicting Dropout in University Students

• Published in: International journal of advanced computer science & applications Vol. 11; no. 1
• Main Authors: Bedregal-Alpaca, Norka, Cornejo-Aparicio, Víctor, Zárate-Valderrama, Joshua, Yanque-Churo, Pedro
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2020
• Subjects: Algorithms; Artificial neural networks; Classification; Colleges & universities; Decision trees; Neural networks; Performance measurement; Risk; Students
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2020.0110133

Academic performance is a topic studied not only to identify those students who could drop out of their studies, but also to classify them according to the type of academic risk they could find themselves. An application has been implemented that uses academic information provided by the university and generates classification models from three different algorithms: artificial neural networks, ID3 and C4.5. The models created use a set of variables and criteria for their construction and can be used to classify student desertion and more specifically to predict their type of academic risk. The performance of these models was compared to define the one that provided the best results and that will serve to make the classification of students. Decision tree algorithms, C4.5 and ID3, presented better measurements with respect to the artificial neural network. The tree generated using the C4.5 algorithm presented the best performance metrics with correctness, accuracy, and sensitivity equal to 0.83, 0.87, and 0.90 respectively. As a result of the classification to determine student desertion it was concluded, according to the model generated using the C4.5 algorithm, that the ratio of credits approved by a student to the credits that he should have taken is the variable more significant. The classification, depending on the type of academic risk, generated a tree model indicating that the number of abandoned subjects is the most significant variable. The admission scan modality through which the student entered the university did not turn out to be significant, as it does not appear in the generated decision tree.