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  • 标题:Personalized recommender system for e-Learning environment based on student’s preferences
  • 本地全文:下载
  • 作者:Hanaa EL FAZAZI ; Mohammed QBADOU ; Intissar SALHI
  • 期刊名称:International Journal of Computer Science and Network Security
  • 印刷版ISSN:1738-7906
  • 出版年度:2018
  • 卷号:18
  • 期号:10
  • 页码:173-178
  • 出版社:International Journal of Computer Science and Network Security
  • 摘要:Nowadays, new technologies and the fast increase of the Internet have made access to information easier for all kind of people, building new challenges for education when utilizing the Internet as a tool. One of the best examples is how to personalize an e-learning system according to the learner’s requirements and knowledge level in a learning process. This system should adapt the learning experience according to the goals of the individual learner. In this paper, we present a recommender e-learning approach which utilizes recommendation techniques for educational data mining specifically for identifying e-Learners’ learning preferences. The proposed approach is based on three modules, a domain module which contains all the knowledge for a particular area, a learner module which uses to identify learners’ learning preferences and activities and a recommendation module which pre-processes data to create a suitable recommendation list and predicting performances. Recommended resources are obtained by using level of knowledge of learners in different steps and the range of recommendation techniques based on content-based filtering and collaborative approaches. Several techniques such as classification, clustering and association rules are used to improve personalization with filtering techniques to provide a recommendation and assist learners to improve their performance.
  • 关键词:E-learning; recommender system; educational data mining; collaborative filtering; learning objects
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