PLORS: a personalized learning object recommender system

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• 作者：Hazra Imran ; Mohammad Belghis-Zadeh ; Ting-Wen Chang…
• 关键词：Personalization ; E ; learning ; Learning management systems ; Recommender system ; Association rule mining ; Learning objects
• 刊名：Vietnam Journal of Computer Science
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：3
• 期：1
• 页码：3-13
• 全文大小：1,483 KB
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• 作者单位：Hazra Imran (1)
  Mohammad Belghis-Zadeh (1)
  Ting-Wen Chang (2)
  Kinshuk (1)
  Sabine Graf (1)
  
  1. Athabasca University, Edmonton, Canada
  2. Beijing Normal University, Beijing, China
  
• 刊物类别：Information Systems and Communication Service; Artificial Intelligence (incl. Robotics); Computer Ap
• 刊物主题：Information Systems and Communication Service; Artificial Intelligence (incl. Robotics); Computer Applications; e-Commerce/e-business; Computer Systems Organization and Communication Networks; Computa
• 出版者：Springer Berlin Heidelberg
• ISSN：2196-8896

文摘

Learning management systems (LMS) are typically used by large educational institutions and focus on supporting instructors in managing and administrating online courses. However, such LMS typically use a “one size fits all” approach without considering individual learner’s profile. A learner’s profile can, for example, consists of his/her learning styles, goals, prior knowledge, abilities, and interests. Generally, LMSs do not cater individual learners’ needs based on their profile. However, considering learners’ profiles can help in enhancing the learning experiences and performance of learners within the course. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners to increase their learning. In this paper, we introduce the personalized learning object recommender system. The proposed system supports learners by providing them recommendations about which learning objects within the course are more useful for them, considering the learning object they are visiting as well as the learning objects visited by other learners with similar profiles. This kind of personalization can help in improving the overall quality of learning by providing recommendations of learning objects that are useful but were overlooked or intentionally skipped by learners. Such recommendations can increase learners’ performance and satisfaction during the course. Keywords Personalization E-learning Learning management systems Recommender system Association rule mining Learning objects