Thursday 22nd of February 2018

Learning Style Deriving Approach to Personalize E-Learning Material Resources

Ibrahim Fathy Moawad

Personalized recommender systems in e-learning environment make the learning process more effective and efficient. In this paper, we present a detailed knowledge-based system design for personalizing the e-learning material resources. Initially, the approach rates the different learning styles according to the learner personal data and preferences, and then it personalizes the learning material resource type, the material abstraction level, and the learning session time. The learning material resources are recommended to the learner in two alternatives: the learning material resources of the most-ranked learning style to learner or an ordered list of material learning resources based on the learning styles ranking. To show how the approach is very beneficial in e-learning systems, we present a case study with different usage scenarios.

Keywords: Recommender System, Personalization, E-learning, Learning Styles, Learning Profile, Domain Knowledge

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Ibrahim Fathy Moawad
Ibrahim Fathy Moawad is an Assistant Professor at the Faculty of Computer and Information Sciences, Ain Shams University, Egypt. He is interested in Artificial Intelligence and Software Engineering research areas. He has published more than 30 technical and academic articles in both international journals and conferences. He is the author of the \"A Framework for Multi-agent Diagnosis System: Argumentation-based Negotiation Technique for Belief Conflict Resolution\" book. He had the best paper award of the 2012 International Conference on Computer Engineering and Systems (ICCES\'2012).

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