Designing an intelligent recommender system using partial credit model and bayesian rough set
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 9, Issue 2 (31 Mar. 2012), pp.179-187, 9 p.
Publisher
Publication Date
2012-03-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Recommender systems have become fundamental in web-based applications and information access.
They effectively prune large information spaces and provide appropriate decision making and suggestions so that users are directed toward those items that best meet their needs, preferences and interests.
In web-based learning context, these systems usually neglect the learner's ability, the difficulty level of the recommended item (e.g., learning resource, exam), and the learner self-assessment.
Therefore, this paper suggests an intelligent recommendation system to provide adaptive learning.
The suggested system consists of two main intelligent agents.
First, a personalized learning resource based on partial credit model (PLR-PCM) agent which considers both the learner's ability and the learning resource difficulty to provide individual learning paths for learners.
Second, BRS-Recommendation agent provides decision rules as instrument or guide for the learner's self-assessment using Bayesian Rough Set (BRS), based on inductive learning algorithm.
Experimental results show that the proposed system can exactly provide a learning resource closer to the learner's ability with appropriate feedback to the learner, resulting in the improvements of the learning efficiency and performance.
American Psychological Association (APA)
Abbas, Iyad Ibrahim& Liu, Juan. 2012. Designing an intelligent recommender system using partial credit model and bayesian rough set. The International Arab Journal of Information Technology،Vol. 9, no. 2, pp.179-187.
https://search.emarefa.net/detail/BIM-292633
Modern Language Association (MLA)
Abbas, Iyad Ibrahim& Liu, Juan. Designing an intelligent recommender system using partial credit model and bayesian rough set. The International Arab Journal of Information Technology Vol. 9, no. 2 (Mar. 2012), pp.179-187.
https://search.emarefa.net/detail/BIM-292633
American Medical Association (AMA)
Abbas, Iyad Ibrahim& Liu, Juan. Designing an intelligent recommender system using partial credit model and bayesian rough set. The International Arab Journal of Information Technology. 2012. Vol. 9, no. 2, pp.179-187.
https://search.emarefa.net/detail/BIM-292633
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 186
Record ID
BIM-292633