Personalized e-learning system using Item Response Theory
Computers & Education Volume 44, Number 3, ISSN 0360-1315 Publisher: Elsevier Ltd
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.
Chen, C.M., Lee, H.M. & Chen, Y.H. (2005). Personalized e-learning system using Item Response Theory. Computers & Education, 44(3), 237-255. Elsevier Ltd.
Cited ByView References & Citations Map
Anke Endler, Gunter Rey, Martin Butz, #252;nter Rey & Martin Butz
Australasian Journal of Educational Technology Vol. 28, No. 7 (Jan 01, 2012)
Antonio Granito, Giuseppina Mangione, Sergio Miranda, Francesco Orciuoli & Pierluigi Ritrovato
Journal of e-Learning and Knowledge Society Vol. 10, No. 1 (Jan 25, 2014)
Adaptation criteria for the personalised delivery of learning materials: A multi-stage empirical investigation
Stefan Thalmann, University of Innsbruck School of Management Information Systems
Australasian Journal of Educational Technology Vol. 30, No. 1 (Apr 03, 2014)
Nick-Naser Manochehri & Khurram Sharif, Qatar University, Qatar
Journal of Information Technology Education: Research Vol. 9, No. 1 (Jan 01, 2010) pp. 31–52
Abuagila Musa & Melvin Ballera, Sirt University, Libya
Society for Information Technology & Teacher Education International Conference 2011 (Mar 07, 2011) pp. 569–574
Chuang-Kai Chiou, Institute of Engineering and Science, Chung Hua University, Hsinchu, 300, Taiwan, ROC, Taiwan; Chao-Hsiang Chen & Judy C.R. Tseng, Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu, 300, Taiwan, ROC, Taiwan; Gwo-Jen Hwang, Department of Information and Learning Technology, National University of Tainan, Tainan, 700, Taiwan, R.O.C., Taiwan
EdMedia + Innovate Learning 2008 (Jun 30, 2008) pp. 2056–2064
These links are based on references which have been extracted automatically and may have some errors. If you see a mistake, please contact firstname.lastname@example.org.