Effective Trust-aware E-learning Recommender System based on Learning Styles and Knowledge Levels

In the age of information explosion, e-learning recommender systems (ELRSs) have emerged as the most essential tool to deliver personalized learning resources to learners. Due to enormous amount of information on the web, learner faces problem in searching right information. ELRSs deal with the problem of information overload effectively and provide recommendations by taking into consideration the learners’ preferences such as learning styles, goals, knowledge levels, learning paths etc. In this paper, we propose a weighted hybrid scheme to recommend right learning resources to a learner by incorporating both the learners’ learning styles (LSs) and the knowledge levels (KLs). Further, by elicitation of trust values among learners, we develop a scheme such that for a given active learner, the trustworthy learners having greater knowledge and similar learning style patterns as that of the active learner have greater weightage in recommendation strategy. Experimental results are presented to demonstrate the effectiveness of the proposed scheme.