A framework to provide personalization in learning management systems through a recommender system approach

Hazra Imran, Quang Hoang, Ting Wen Chang, Kinshuk, Sabine Graf

Research output: Contribution to journalConference articlepeer-review

14 Citations (Scopus)

Abstract

Personalization in learning management systems (LMS) occurs when such systems tailor the learning experience of learners such that it fits to their profiles, which helps in increasing their performance within the course and the quality of learning. A learner's profile can, for example, consist of his/her learning styles, goals, existing knowledge, ability and interests. Generally, traditional LMSs do not take into account the learners' profile and present the course content in a static way to every learner. To support personalization in LMS, recommender systems can be used to recommend appropriate learning objects to learners, not only based on their individual profile but also based on what worked well for learners with a similar profile. In this paper, we propose a framework to integrate a recommender system approach into LMS. The proposed framework is designed with the goal of presenting a flexible integration model which can provide personalization by automatically suggesting learning objects to learners based on their current situation as well as successful learning experiences of learners with similar profiles in a similar situation. Such advanced personalization can help learners in many ways such as reducing the learning time without negative impact on their marks, improving learning performance as well as increasing the level of satisfaction.

Original languageEnglish
Pages (from-to)271-280
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8397 LNAI
Issue numberPART 1
DOIs
Publication statusPublished - 2014
Event6th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2014 - Bangkok, Thailand
Duration: 7 Apr. 20149 Apr. 2014

Keywords

  • E-Learning
  • Learning Management Systems
  • Personalization
  • Recommender System

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