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ERIC Number: EJ905060
Record Type: Journal
Publication Date: 2010-Dec
Pages: 17
Abstractor: As Provided
Reference Count: 29
ISSN: ISSN-1042-1629
Learning Materials Recommendation Using Good Learners' Ratings and Content-Based Filtering
Ghauth, Khairil Imran; Abdullah, Nor Aniza
Educational Technology Research and Development, v58 n6 p711-727 Dec 2010
The enormity of the amount of learning materials in e-learning has led to the difficulty of locating suitable learning materials for a particular learning topic, creating the need for recommendation tools within a learning context. In this paper, we aim to address this need by proposing a novel e-learning recommender system framework that is based on two conceptual foundations--peer learning and social learning theories that encourage students to cooperate and learn among themselves. Our proposed framework works on the idea of recommending learning materials with a similar content and indicating the quality of learning materials based on good learners' ratings. A comprehensive set of experiments were conducted to measure the system accuracy and its impact on learner's performance. The obtained results show that the proposed e-learning recommender system has a significant improvement in the post-test of about 12.16% with the effect size of 0.6 and 13.11% with the effect size of 0.53 when compared to the e-learning with a content-based recommender system and the e-learning without a recommender system, respectively. Furthermore, the proposed recommender system performed better in terms of having a small rating deviation and a higher precision as compared to e-learning with a content-based recommender system. (Contains 1 table and 8 figures.)
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A