NotesFAQContact Us
Collection
Advanced
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ930252
Record Type: Journal
Publication Date: 2011
Pages: 10
Abstractor: As Provided
Reference Count: 25
ISBN: N/A
ISSN: ISSN-1436-4522
The Effect of Incorporating Good Learners' Ratings in e-Learning Content-Based Recommender System
Ghauth, Khairil Imran; Abdullah, Nor Aniza
Educational Technology & Society, v14 n2 p248-257 2011
One of the anticipated challenges of today's e-learning is to solve the problem of recommending from a large number of learning materials. In this study, we introduce a novel architecture for an e-learning recommender system. More specifically, this paper comprises the following phases i) to propose an e-learning recommender system based on content-based filtering and good learners' ratings, and ii) to compare the proposed e-learning recommender system with exiting e-learning recommender systems that use both collaborative filtering and content-based filtering techniques in terms of system accuracy and student's performance. The results obtained from the test data show that the proposed e-learning recommender system outperforms existing e-learning recommender systems that use collaborative filtering and content-based filtering techniques with respect to system accuracy of about 83.28% and 48.58%, respectively. The results further show that the learner's performance is increased by at least 12.16% when the students use the e-learning with the proposed recommender system as compared to other recommendation techniques. (Contains 2 tables and 8 figures.)
International Forum of Educational Technology & Society. Athabasca University, School of Computing & Information Systems, 1 University Drive, Athabasca, AB T9S 3A3, Canada. Tel: 780-675-6812; Fax: 780-675-6973; Web site: http://www.ifets.info
Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A