Quantcast

PLORS: a personalized learning object recommender system

Research paper by Hazra Imran, Mohammad Belghis-Zadeh, Ting-Wen Chang, Kinshuk, Sabine Graf

Indexed on: 27 Aug '15Published on: 27 Aug '15Published in: Vietnam Journal of Computer Science



Abstract

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.