Abstract
Objectives: To make a systematic review of literature on the prediction of university student dropout through data mining techniques. Methods/Analysis: The study was developed as a systematic review of the literature of empirical research results regarding the prediction of university dropout. In this phase, the review protocol, the selection requirements for potential studies and the method for analyzing the content of the selected studies were provided. The classification presented in section 3 allowed answering the main research question. What are the aspects considered in the prediction of university student desertion through data mining? Findings: University dropout is a problem which affects universities around the world, with consequences such as reduced enrolment, reduced revenue for the university, and financial losses for the State which funds the studies, and also constitutes a social problem for students, their families, and society in general. Hence the importance of predicting university dropout, that is to say identify dropout students in advance, in order to design strategies to tackle this problem. Novelty /Improvement: This is the first work to perform an integral systematic literature review about university dropout prediction through data mining, with studies from 2006–2018. Keywords: Data Mining, Dropout Factors, Dropout Prediction, Machine Learning, University Student Dropout
Highlights
There is currently an increasing interest in researching the topic of university dropout around the world[1], with one of the main concerns being elevated rates of occurrence[2]
The present study aims to answer the following question: What aspects are considered in predicting university student dropout through data mining? To meet this objective, we propose a systematic literature review of the period 2006–2018, including journals indexed in Scimago Journal & Country Rank, from which we identified and analyzed 67 articles from nine academic publishers
We identified three aspects regarding university dropout prediction: factors, techniques and tools, all of which are specified in the framework of the present study
Summary
There is currently an increasing interest in researching the topic of university dropout around the world[1], with one of the main concerns being elevated rates of occurrence[2]. Dropout negatively affects institutions in the reduction of enrolment and the non-achievement of institutional objectives[3]. Students, universities and governments are affected in both economic and social terms. Dropout becomes a critical topic when university administrators do not possess the tools necessary to identify students who are at risk of leaving the institution. The early prediction of student dropout has become a major challenge, as well as identifying the factors which contribute to this increasingly occurring phenomenon[6].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
