A Withdrawal Prediction Model of At-Risk Learners Based on Behavioural Indicators

Autor/inn/en	Hlioui, Fedia; Aloui, Nadia; Gargouri, Faiez
Titel	A Withdrawal Prediction Model of At-Risk Learners Based on Behavioural Indicators
Quelle	In: International Journal of Web-Based Learning and Teaching Technologies, 16 (2021) 2, S.32-53, Artikel 3 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1548-1093
DOI	10.4018/IJWLTT.2021030103
Schlagwörter	At Risk Students; Withdrawal (Education); Dropouts; Distance Education; Teaching Methods; Guidelines; Open Universities; Semantics; Student Behavior; Competition; Prediction; Models; Student Characteristics; Data Analysis; Personal Autonomy; Academic Persistence; Cooperative Learning; Course Content; Learning Motivation; Educational Indicators + Suchen Sie Ihr Suchwort? Kursabbruch; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Distance study; Distance learning; Fernunterricht; Teaching method; Lehrmethode; Unterrichtsmethode; Richtlinien; Offene Universität; Semantik; Student behaviour; Schülerverhalten; Wettkampf; Vorhersage; Analogiemodell; Auswertung; Individuelle Autonomie; Kooperatives Lernen; Kursprogramm; Motivation for studies; Lernmotivation; Educational indicato; Bildungsindikator
Abstract	Nowadays, the virtual learning environment has become an ideal tool for professional self-development and bringing courses for various learner audiences across the world. There is currently an increasing interest in researching the topic of learner dropout and low completion in distance learning, with one of the main concerns being elevated rates of occurrence. Therefore, the early prediction of learner withdrawal has become a major challenge, as well as identifying the factors, which contribute to this increasingly occurring phenomenon. In that regard, this manuscript presents a framework for withdrawal prediction model for the data from The Open University, one of the largest distance learning institutions. For that purpose, we start by pre-processing the dataset and tackling the challenge of discretization process and unbalanced data. Secondly, this paper identifies the semantical issues of raw data by introducing new behavioural indicators. Finally, we reckon on machine learning algorithms for withdrawal prediction model to understand the lack of learners' commitment at an early stage. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01