Discovering Learners' Characteristics through Cluster Analysis for Recommendation of Courses in E-Learning Environment

Autor/inn/en	Rawat, Bhupesh; Dwivedi, Sanjay K.
Titel	Discovering Learners' Characteristics through Cluster Analysis for Recommendation of Courses in E-Learning Environment
Quelle	In: International Journal of Information and Communication Technology Education, 15 (2019) 1, S.42-66, Artikel 4 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1550-1876
DOI	10.4018/IJICTE.2019010104
Schlagwörter	Electronic Learning; Student Characteristics; Learning Processes; Management Systems; Classification; Data Analysis; Higher Education; Multivariate Analysis; Accuracy; Academic Achievement; Online Courses; Preferences; Course Selection (Students); Assignments; Tests + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Classification system; Klassifikation; Klassifikationssystem; Auswertung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Multivariate Analyse; Schulleistung; Online course; Online-Kurs; Course selection; Student; Students; Kurswahl; Assignment; Auftrag; Zuweisung; Examination; Prüfung; Examen
Abstract	With the emergence of the web, traditional learning has changed significantly. Hence, a huge number of 'e-learning systems' with the advantages of time and space have been created. Currently, many e-learning systems are being used by a large number of academic institutions worldwide which allow different users of the system to perform various tasks based on their goals. However, most of these systems follow a 'one size fits all' approach where same resources are offered to learners irrespective of their unique learning requirements. Therefore, personalization is required as a part of e-learning systems which offers resources to learners based on their profile. This research aims to perform cluster analyses in order to validate clusters created through a k-means algorithm. The clusters will be used to classify a new learner into its appropriate class and recommend relevant courses. Finally, the accuracy of the recommendation is evaluated using various evaluation metrics. The proposed recommendation system helps learners to improve their academic performance and hence overall learning process as well. (As Provided).
Anmerkungen	IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01