A Fuzzy Classification Approach for Learning Style Prediction Based on Web Mining Technique in E-Learning Environments

Autor/inn/en	El Aissaoui, Ouafae; El Alami El Madani, Yasser; Oughdir, Lahcen; El Allioui, Youssouf
Titel	A Fuzzy Classification Approach for Learning Style Prediction Based on Web Mining Technique in E-Learning Environments
Quelle	In: Education and Information Technologies, 24 (2019) 3, S.1943-1959 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (El Allioui, Youssouf)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-018-9820-5
Schlagwörter	Classification; Cognitive Style; Models; Electronic Learning; Learning Processes; Self Evaluation (Individuals); Student Attitudes; Student Behavior; Preferences; Recordkeeping; Data Analysis; Computer Software + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Cognitive styles; Kognitiver Stil; Analogiemodell; Learning process; Lernprozess; Schülerverhalten; Student behaviour; Leistungsnachweis; Auswertung
Abstract	Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of learners of their own preferences. Furthermore, the vast majority of learners experience boredom when they are asked to fill out the corresponding questionnaire. Besides that, traditional approach assumes that learning styles are fixed, and cannot change over time. In this paper, we propose a generic approach for detecting learning styles automatically according to a given learning styles model. In fact, our approach does not depend on a specific LSM. This work consists of two major steps. First, we extract learning sequences from learners log files using web usage mining techniques. Second, we classify the extracted learners' sequences according to a specific learning style model using clustering algorithms. To perform our approach we use Felder-Silverman Model as LSM and Fuzzy C-Means as a clustering algorithm. We have conducted an experimental study using a real-world dataset. The obtained results show that our approach outperforms traditional approach and provides promising results. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01