A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction

In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characteriz...

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Bibliographic Details
Published in:Higher education studies 2020-09, Vol.10 (4), p.12
Main Authors: Nicoletti, Maria do Carmo, Oliveira, Osvaldo Luiz de
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computation
Computer System Design
Identification
Man Machine Systems
Potential Dropouts
Prediction
Undergraduate Students
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Summary:In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course leaving the educational institution without finishing the course. This paper describes the proposal of the architecture of a computational system, PDE (Predicting Dropout Events), based on machine learning (ML) algorithms and specifically designed for predicting dropout events in a higher level educational environment. PDE's main subsystem implements a group of instance-based learning (IBL) algorithms which, taking into account a particular university-course environment, and based on log files containing descriptions of previous dropouts events, is capable to predict when a student already engaged in the course, is prone to dropout, so preventive measures could be quickly implemented.
ISSN:1925-4741
1925-475X
DOI:10.5539/hes.v10n4p12