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

Autor/inn/en	Nicoletti, Maria do Carmo; de Oliveira, Osvaldo Luiz
Titel	A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction
Quelle	In: Higher Education Studies, 10 (2020) 4, S.12-24 (13 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1925-4741
Schlagwörter	Artificial Intelligence; Man Machine Systems; Computation; Prediction; Potential Dropouts; Undergraduate Students; Identification; Computer System Design + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Mensch-Maschine-System; Vorhersage; Identifikation; Identifizierung
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01