Discovering Learners Behaviour Patterns from Log Files Using LSA

Autor/inn/en	Milat, Iness Nedji; Seridi, Hassina; Moudjari, Abdelkader
Titel	Discovering Learners Behaviour Patterns from Log Files Using LSA
Quelle	In: International Journal of Distance Education Technologies, 18 (2020) 2, S.90-113, Artikel 6 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1539-3100
DOI	10.4018/IJDET.2020040106
Schlagwörter	Student Behavior; Behavior Patterns; Electronic Learning; Learning Analytics; Natural Language Processing; College Freshmen + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Natürliche Sprache; Studienanfänger
Abstract	Recently, discovering learner behaviour has taken more attention in the field of e-learning. It aims to gain useful insights into the learning process of students despite the absence of direct interaction with teachers. In fact, the only available source of information in such environments is the log file that represents all possible interactions of learners with the e-learning system. This log file is characterised by the presence of noise, incomplete information, and a huge amount of data. In this article, a new approach based on learner trails analysis from the log file is proposed. It aims to discover the patterns of the real behaviour of learners and to determine their pedagogic orientations. The latent semantic analysis (LSA) method is used to extract the relationship between learners who have the same behaviour and to overcome the noise problem. The proposed approach has been validated using synthetic and genuine log files. The obtained results show the efficiency of the proposed method of discovering the behaviours of learners. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01