Accessing e-Learners' Knowledge for Personalization in e-Learning Environment

e-Learning has become a trend in the world nowadays. However, most researches neglect a fundamental issue - the e-Learner's prior knowledge on which the useful intelligent systems are based. This research employs the e-Learner's prior knowledge and mines his/her interior desire on appropriate target courses or materials as a part of a personalization process to construct the overall e-Learning strategy for education. This paper illustrates a novel web usage mining approach, based on the sequence mining technique applied to e-Learner's navigation behaviour, to discover patterns in the navigation of e-Learning websites. Three critical contributions are made in this paper: (1) using the footstep graph to visualize the e-Learner's click-stream data so any interesting pattern can be detected more easily and quickly; (2) illustrating a novel sequence mining approach to identify pre-designated e-Learner navigation patterns automatically and integrating a back-propagation network (BPN) model smoothly; and (3) applying the empirical research to indicate that the proposed approach can predict and categorize the e-Learners' navigation behaviour with high accuracy.