A Predictive Analytics Framework as a Countermeasure for Attrition of Students

Autor/inn/en	Gontzis, Andreas F.; Kotsiantis, Sotiris; Panagiotakopoulos, Christos T.; Verykios, Vassilios S.
Titel	A Predictive Analytics Framework as a Countermeasure for Attrition of Students
Quelle	In: Interactive Learning Environments, 30 (2022) 3, S.568-582 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Gontzis, Andreas F.) ORCID (Kotsiantis, Sotiris) ORCID (Panagiotakopoulos, Christos T.) ORCID (Verykios, Vassilios S.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2019.1674884
Schlagwörter	Student Attrition; Distance Education; At Risk Students; Achievement; Prediction; College Students; Information Retrieval; Pattern Recognition; Data Analysis; Models + Suchen Sie Ihr Suchwort? Schülerbeurlaubung; Distance study; Distance learning; Fernunterricht; Performance; Leistung; Vorhersage; Collegestudent; Mustererkennung; Auswertung; Analogiemodell
Abstract	Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure, supporting self-directed learning. Despite the extensive application of data mining to education, the imbalance problem in minority classes of students' attrition is often overlooked in conventional models. This document proposes a large data frame using the Hadoop ecosystem and the application of machine learning techniques to different datasets of an academic year at the Hellenic Open University. Datasets were divided into thirty-five weeks. Thirty-two classifiers were created, compared and statistical analyzed to address the minority classes' imbalance of student's failure. The algorithms metacost-SMO, and C4.5 provide the most accurate performance for each target class. Early predictions of timeframes determine a remarkable performance, while the importance of written assignments and specific quizzes is noticeable. The models' performance in any week is exploited by developing a prediction tool for student attrition, contributing to timely and personalized intervention. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01