Establishing a Probabilistic Model for Cognitive Learning Style - Teh, Yoke-Jin; Somnuk, Phon-Amnuaisuk; Ting, Choo-Yee (IOS Press Amsterdam, The Netherlands, The Netherlands, 2005)
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.