Personalized SCORM Learning Experience Based on Rating Scale Model
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Design E-learning Recommendation System Using PIRT and VPRS Model
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Research and design of a grid based electronic commerce recommendation system
Journal of Theoretical and Applied Electronic Commerce Research
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Semantic caching for web based learning systems
ICWL'05 Proceedings of the 4th international conference on Advances in Web-Based Learning
Expert Systems with Applications: An International Journal
A web-based e-testing system supporting test quality improvement
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
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With the rapid growth of computer and Internettechnologies, e-learning has become a major trend in thecomputer assisted teaching and learning field currently. Inpast years, many researchers made efforts in developing e-learningsystems with personalized learning mechanism toassist on-line learning. However, most of them focused onusing learner's behaviors, interests, or habits to providepersonalized e-learning services. These systems usuallyneglected to concern if learner's ability and the difficultyof courseware are matched each other. Generally,recommending an inappropriate courseware might resultin learner's cognitive overhead or disorientation during alearning process. To promote learning efficiency andeffectiveness, this paper presents a personalizedcourseware recommendation system (PCRS) based on theproposed fuzzy item response theory (FIRT), which canrecommend courseware with appropriate difficult level tolearner through learner gives a fuzzy response ofunderstanding percentage for the learned courseware.Experiment results show that applying the proposed fuzzyitem response theory to Web-based learning can achievepersonalized learning and help learners to learn moreeffectively and efficiently.