Abstract
Based on the premise that university student dropout is a social problem in the university ecosystem of any country, technological leverage is a way that allows us to build technological proposals to solve a poorly met need in university education systems. Under this scenario, the study presents and analyzes eight predictive models to forecast university dropout, based on data mining methods and techniques, using WEKA for its implementation, with a dataset of 4365 academic records of students from the National University of Moquegua (UNAM), Peru. The objective is to determine which model presents the best performance indicators to forecast and prevent student dropout. The study aims to propose and compare the accuracy of eight predictive models with balanced classes, using the SMOTE method for the generation of synthetic data. The results allow us to confirm that the predictive model based on Random Forest is the one that presents the highest accuracy and robustness. This study is of great interest to the educational community as it allows for predicting the possible dropout of a student from a university career and being able to take corrective actions both at a global and individual level. The results obtained are highly interesting for the university in which the study has been carried out, obtaining results that generally outperform the results obtained in related works.
Highlights
The phenomenon of university student dropout has been a research topic for several decades [1]
We focused our analysis on work that applied machine learning and educational data mining (EDM) techniques to predict student dropout, other techniques have been investigated for the detection and adoption of strategies for preventing and reducing student dropout
SH4: The training of a predictive model based on artificial intelligence allows, from the input of factors that characterize the pattern of the student deserting, for significantly detecting student dropout at UNAM
Summary
The phenomenon of university student dropout has been a research topic for several decades [1]. At present, this problem has become more important due to the negative effects caused by COVID-19. The deficient detection of the dropout of students in higher education is an important problem for the managers of the world’s universities [6,7,8]. The situation is exacerbated in countries with low cultural and economic levels and, how to counteract the consequences of university student dropout is studied worldwide [9,10]. Student dropout is a phenomenon that can manifest itself in any academic period [14,15], for which corrective and preventive measures should be proposed aimed at university retention
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