A Machine Learning Based Approach to Enhance MOOC Users' Classification

Autor/inn/en	Mourdi, Youssef; Sadgal, Mohammed; Berrada Fathi, Wafa; El Kabtane, Hamada
Titel	A Machine Learning Based Approach to Enhance MOOC Users' Classification
Quelle	In: Turkish Online Journal of Distance Education, 21 (2020) 2, Artikel 4 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Mourdi, Youssef) ORCID (Sadgal, Mohammed) ORCID (Berrada Fathi, Wafa) ORCID (El Kabtane, Hamada)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1302-6488
Schlagwörter	Online Courses; Classification; Artificial Intelligence; Distance Education; Student Characteristics; Academic Failure; Potential Dropouts; Academic Achievement; Prediction; Accuracy + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Classification system; Klassifikation; Klassifikationssystem; Künstliche Intelligenz; Distance study; Distance learning; Fernunterricht; Schulleistung; Vorhersage
Abstract	At the beginning of the 2010 decade, the world of education and more specifically e-learning was revolutionized by the emergence of Massive Open Online Courses, better known by their acronym MOOC. Proposed more and more by universities and training centers around the world, MOOCs have become an undeniable asset for any student or person seeking to complete their initial training with free distance courses open to all areas. Despite the remarkable number of course enrollees, MOOCs have a huge dropout rate of up to 90%. This rate significantly affects the efforts made by the moderators for the success of this pedagogical model and negatively influences the learners' experience and their supervision. To address this problem and help instructors streamline their interventions, we present a solution to classify MOOC learners into three distinct classes. The approach proposed in this paper is based on the filters methods to select the most relevant attributes and ensembling methods of machine learning algorithms. This approach has been validated by four MOOC courses from Stanford University. In order to prove the performance of the model (92.2%), a comparative study between the proposed model and other algorithms was made on several performance measures. (As Provided).
Anmerkungen	Anadolu University. Office of the Rector, Eskisehir, 26470, Turkey. Tel: +90-222-335-34-53; Fax: +90-222-335-34-86; e-mail: rektor@anadolu.edu.tr; e-mail: TOJDE@anadolu.edu.tr; Web site: http://tojde.anadolu.edu.tr/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01