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A model to detect student’s learning styles in the blended learning course

Nguyen, Viet Anh A model to detect student’s learning styles in the blended learning course. In: The 8th International Conference on Frontiers of Educational Technologies, 10-12 June, 2022, Yokohama, Japan. (In Press)

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Recently, personalized learning is becoming more and more popular, especially in blended learning courses. The student learning style is one of several factors to personalize content and appropriate learning activities for each learner. This article proposes a model to detect learning styles in blended learning courses to classify learners for personalization. The proposed model focuses on two phases: the online and face-to-face learning phases of the learning process. Besides, we also present several parameters to map resources and activities in a blended-learning course to a learning style model. Based on the identified criteria, the experimental results with 205 students' data when classifying learning styles by the Support Vector Machine method give an accuracy of 76.7% - 83.2% in the 04 dimensions of the Felder and Silverman model. Experimental results when applying two approaches: literature-based and driven-based, show that learners' styles are similar up to 83%. Findings show that the student's learning style does not change much in the learning process. As for course design implications, we also propose suggestions for developing content and designing learning activities.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
ISI/Scopus indexed conference
Divisions: Faculty of Information Technology (FIT)
Depositing User: Viet Anh Nguyen
Date Deposited: 22 Aug 2022 03:57
Last Modified: 22 Aug 2022 03:57

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