Hybrid Adaptive Educational Hypermedia ‎Recommender Accommodating User’s Learning ‎Style and Web Page Features‎

• Main Authors: M. Tahmasebi, F. Fotouhi, M. Esmaeili
• Format: Article
• Language: English
• Published: Shahrood University of Technology 2019-04-01
• Series: Journal of Artificial Intelligence and Data Mining
• Subjects: adaptive educational hypermedia; individual learning styles ‎detection; learner modeling; ‎page ranking; ‎recommendation systems.‎
• Online Access: http://jad.shahroodut.ac.ir/article_1190_d31edf47e2f34e5617ed05a80bd9ee71.pdf

Personalized recommenders have proved to be of use as a solution to reduce the information overload ‎problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers ‎suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. ‎Furthermore, obtaining learner’s preferences is cumbersome. Most studies have only focused on similarity ‎between the interest profile of a user and those of others. However, it can lead to the gray-sheep problem, ‎in which users with consistently different opinions from the group do not benefit from this approach. On ‎this basis, matching the learner’s learning style with the web page features and mining specific attributes ‎is more desirable. The primary contribution of this research is to introduce a feature-based recommender ‎system that delivers educational web pages according to the user's individual learning style. We propose an ‎Educational Resource recommender system which interacts with the users based on their learning style ‎and cognitive traits. The learning style determination is based on Felder-Silverman theory. Furthermore, ‎we incorporate all explicit/implicit data features of a page and the elements contained in them that have an ‎influence on the quality of recommendation and help the system make more effective recommendations.‎