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    题名: Personalized E-Learning System Using Item Response Theory
    作者: 陳志銘
    Chen, Chih-Ming 
    Lee, Hahn-Ming
    Chen, Ya-Hui
    贡献者: 圖檔所
    关键词: Distance education;Learning strategies;Intelligent tutoring systems;Collaborative learning
    日期: 2005
    上传时间: 2021-04-22 15:33:19 (UTC+8)
    摘要: Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.
    關聯: Computers & Education, Vol.44, No.3, pp.237-255
    数据类型: article
    DOI 連結: https://doi.org/10.1016/j.compedu.2004.01.006
    DOI: 10.1016/j.compedu.2004.01.006
    显示于类别:[圖書資訊與檔案學研究所] 期刊論文

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