Student Engagement Level in an e-Learning Environment: Clustering Using K-Means

Autor/inn/en	Moubayed, Abdallah; Injadat, Mohammadnoor; Shami, Abdallah; Lutfiyya, Hanan
Titel	Student Engagement Level in an e-Learning Environment: Clustering Using K-Means
Quelle	In: American Journal of Distance Education, 34 (2020) 2, S.137-156 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Moubayed, Abdallah) ORCID (Injadat, Mohammadnoor) ORCID (Shami, Abdallah)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0892-3647
DOI	10.1080/08923647.2020.1696140
Schlagwörter	Learner Engagement; Electronic Learning; Individualized Instruction; Undergraduate Students; Learning Analytics; Identification; Interaction; College Science; Foreign Countries; Models; Program Effectiveness; Student Behavior; Canada + Suchen Sie Ihr Suchwort? Individualisierender Unterricht; Identifikation; Identifizierung; Interaktion; Ausland; Analogiemodell; Student behaviour; Schülerverhalten; Kanada
Abstract	E-learning platforms and processes face several challenges, among which is the idea of personalizing the e-learning experience and to keep students motivated and engaged. This work is part of a larger study that aims to tackle these two challenges using a variety of machine learning techniques. To that end, this paper proposes the use of k-means algorithm to cluster students based on 12 engagement metrics divided into two categories: interaction-related and effort-related. Quantitative analysis is performed to identify the students that are not engaged who may need help. Three different clustering models are considered: two-level, three-level, and five-level. The considered dataset is the students' event log of a second-year undergraduate Science course from a North American university that was given in a blended format. The event log is transformed using MATLAB to generate a new dataset representing the considered metrics. Experimental results' analysis shows that among the considered interaction-related and effort-related metrics, the number of logins and the average duration to submit assignments are the most representative of the students' engagement level. Furthermore, using the silhouette coefficient as a performance metric, it is shown that the two-level model offers the best performance in terms of cluster separation. However, the three-level model has a similar performance while better identifying students with low engagement levels. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01