A Personalized Courseware Recommendation System Based on Fuzzy Item Response Theory
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
Personalized e-learning system using Item Response Theory
Computers & Education
Personalized curriculum sequencing utilizing modified item response theory for web-based instruction
Expert Systems with Applications: An International Journal
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Sharable Content Object Reference Model (SCORM) is the most popular suite of technical standard among existing international standards for e-Learning; although it has been designed to provide accessibility, adaptability, interoperability, and reusability, it still suffers from lack of personalization, which may lead to inappropriate learning experience; In other words, learner may suffer from distraction or restriction, when it comes to interact with large or restricted amounts of information respectively, resulting in reduced learning efficiency and performance. However, this can be avoided by providing personalized services. In this paper, we propose a personalized SCORM learning experience based on Rating Scale Model (RSM), which takes into account both the difficulty of learning activity and the learner's ability considering responses from individual learner's understanding and characteristics. To obtain more accurate estimation of learner's ability, polytomous Item Response Model (IRT) is used rather than dichotomous IRT. Experimental results show that the proposed system can exactly provide the closer learning resource to the learner's ability, resulting in increased the learning efficiency and learning performance.