Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Temporary user modeling for adaptive product presentations in the Web
UM '99 Proceedings of the seventh international conference on User modeling
Learning users' interests by unobtrusively observing their normal behavior
Proceedings of the 5th international conference on Intelligent user interfaces
Proceedings of the 6th international conference on Intelligent user interfaces
Equilibriating instructional media for cognitive styles
Proceedings of the 8th annual conference on Innovation and technology in computer science education
Enhancing student learning through hypermedia courseware andincorporation of student learning styles
IEEE Transactions on Education
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Current focus in education systems is to develop SCORM-based ITS that is able to adapt instructional contents to student's learning method and preferences. In order to adapt to the student's preference, the ITS must be able to capture the student's preferred learning style. However, this involves inherent uncertainty in understanding and categorizing student's learning style. This paper highlights the probabilistic model of student's cognitive learning style and its integration into SCORM e-learning environment. Our student model uses Bayesian Networks to handle uncertainties by computing a probabilistic assessment of predicting the student's cognitive learning style based on the student's interactions captured by the SCORM CAM and SCORM RTE commands. Towards the end of this paper, a review of student model and preliminary investigation to assess and evaluate our initial network is presented.