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.
Highlights
Adaptive systems provide and adapt contents according to the properties of an individual user [1]
E user model in these systems is confined and focuses on the features that are associated with the learning aspect of a student [2, 3], and learning styles are exploited in the adaptive hypermedia domain as a source of content adaptation [4,5,6]
For the effectiveness of learning systems in the educational domain such as adaptive hypermedia, learning styles are used for content adaptation according to the user learning style
Summary
Adaptive systems provide and adapt contents according to the properties of an individual user [1]. E user model in these systems is confined and focuses on the features that are associated with the learning aspect of a student [2, 3], and learning styles are exploited in the adaptive hypermedia domain as a source of content adaptation [4,5,6]. To address a domain-wise aspect of learning styles, we have proposed a domain-wise learning style solution that effectively exploits a student’s discipline-specific learning style preferences for content adaptation. (i) To exploit domain-wise learning preferences for effective learning (ii) To propose a model which has the capability to adjust domain-wise learning style variations for different subject domains (iii) To provide a computationally efficient solution that can be adopted for developing learning systems in the educational hypermedia domain e rest of the paper is divided into the following sections.
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