Per sonalized E-learning System by Using Item Response Theory

Abstract

Personalized service is an important issue on the Web, especially on Web-based learning. In general, most personalized systems only consider learners’ preferences, interests, and browsing behaviors to provide personalized services. They do not consider learners’ abilities as an important factor to implement personalized mechanism. Besides, too many hyperlink structures on Web-based learning systems bring a lot of information burdens to learners. Hence, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of adaptive mechanism, the information overload problem, and the adaptation of courses materials difficulties are main research issues. Thus, we consider both the difficulties of course materials and learners’ abilities to provide personalized learning. This study adopts Item Response Theory (IRT) to estimate the learners’ abilities and rank appropriate course materials to learners. We also propose a collaborative voting approach to adjust the difficulties of the course materials in order to determine the difficulties of the course materials more objectively. Furthermore, learners’ abilities can be adjusted according to learners’ explicit feedback to achieve the goal of the personalized course materials’ recommendation. Experimental results show that IRT applied to Web learning can achieve personalized learning and assist learners to learn more effectively and efficiently.