The effect of incorporating good learners' ratings in e-learning content-based recommender system - Ghauth, K.I.; Abdullah, N.A. (2011)
One of the anticipated challenges of todays e-learning is to solve the problem of recommending from a large number of learning materials. In this study, we introduce a novel architecture for an e-learning recommender system. More specifically, this paper comprises the following phases i) to propose an e-learning recommender system based on content-based filtering and good learnersratings, and ii) to compare the proposed e-learning recommender system with exiting e-learning recommender systems that use both collaborative filtering and content-based filtering techniques in terms of system accuracy and students performance. The results obtained from the test data show that the proposed e-learning recommender system outperforms existing e-learning recommender systems that use collaborative filtering and content-based filtering techniques with respect to system accuracy of about 83.28 and 48.58, respectively. The results further show that the learners performance is increased by at least 12.16 when the students use the e-learning with the proposed recommender system as compared to other recommendation techniques.