A Computationally Efficient User Model for Effective Content Adaptation Based on Domain‐Wise Learning Style Preferences: A Web‐Based Approach

Abstract

In the educational hypermedia domain, adaptive systems try to adapt educational materials according to the required properties of a user. The adaptability of these systems becomes more effective once the system has the knowledge about how a student can learn better. Studies suggest that, for effective personalization, one of the important features is to know precisely the learning style of a student. However, learning styles are dynamic and may vary domain-wise. To address such aspects of learning styles, we have proposed a computationally efficient solution that considers the dynamic and nondeterministic nature of learning styles, effect of the subject domain, and nonstationary aspect during the learning process. The proposed model is novel, robust, and flexible to optimize students’ domain-wise learning style preferences for better content adaptation. We have developed a web-based experimental prototype for assessment and validation. The proposed model is compared with the existing available learning style-based model, and the experimental results show that personalization based on incorporating discipline-wise learning style variations becomes more effective.

Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

“…Learning style VARK questionnaire [29], [36] Learning style ILS questionnaire [18], [19], [40], [41], [43]- [45], [25], [26], [28], [32]- [34], [37], [38] Learning style Questionnaire -80 questions (Yes/No) [24] Learning style Facial images detection -VARK Learning style model [30] Learning style Web log file -FSLMS [27] Learning style Comparative analysis model (Felder & Silverman, Kolb, VARK, and Honey & Mumford) [39] Learning style Automatic online identification -Honey and Mumford [7] Learning style Web log analysis: type of file accessed, the time spent, the total number of times each file accessed - [31] FSLSM Learning style…”