Measuring learner's performance in e-learning recommender systems

Ghauth, Khairil Imran and Abdullah, Nor Aniza (2010) Measuring learner's performance in e-learning recommender systems. Australasian Journal of Educational Technology, 26 (6). pp. 764-774. ISSN 1449-5554, DOI


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A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.

Item Type: Article
Uncontrolled Keywords: Recommender system; collaborative filtering (by user ratings); content-based filtering (by keywords);hybrid filtering;e-learning;e-learning recommender system framework
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Miss Nur Jannatul Adnin Ahmad Shafawi
Date Deposited: 07 Feb 2013 01:20
Last Modified: 07 Nov 2019 04:51

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