A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments
Autor: | Yasser El Alami El Madani, Lahcen Oughdir, Ouafae El Aissaoui, Youssouf El Allioui |
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Rok vydání: | 2018 |
Předmět: |
Fuzzy classification
Computer science business.industry E-learning (theory) 05 social sciences Educational technology 050301 education Personalized learning Library and Information Sciences Machine learning computer.software_genre Education Learning styles Web mining 0502 economics and business 050211 marketing Artificial intelligence business Cluster analysis 0503 education computer Cognitive style |
Zdroj: | Education and Information Technologies. 24:1943-1959 |
ISSN: | 1573-7608 1360-2357 |
DOI: | 10.1007/s10639-018-9820-5 |
Popis: | 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. |
Databáze: | OpenAIRE |
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