WEBLORS – A Personalized Web-Based Recommender System
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Nowadays, personalization and adaptivity becomes more and more important in most systems. When it comes to education and learning, personalization can provide learners with better learning experiences by considering their needs and characteristics when presenting them with learning materials within courses in learning management systems. One way to provide students with more personal learning materials is to deliver personalized content from the web. However, due to information overload, finding relevant and personalized materials from the web remains a challenging task. This paper presents an adaptive recommender system called WEBLORS that aims at helping learners to overcome the information overload by providing them with additional personalized learning materials from the web to increase their learning and performance. This paper also presents the evaluation of WEBLORS based on its recommender system acceptance using data from 36 participants. The evaluation showed that overall, participants had a positive experience interacting with WEBLORS. They trusted the recommendations and found them helpful to improve learning and performance, and they agreed that they would like to use the system again.
KeywordsRecommender systems Web mining Personalization
The authors acknowledge the support of Athabasca University, Alberta Innovates – Technology Futures (AITF), Ministry of Advanced Education of Canada, the National Science and Engineering Research Council of Canada (NSERC) [funding reference number: 402053-2012-RGPIN], and Mitacs.
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