Deriving Prior Distributions for Bayesian Models Used to Achieve Adaptive E-Learning

Abstract: This paper presents an approach of achieving adaptive e-learning by probabilistically evaluating a learner based not only on the profile and performance data of the learner but also on the data of previous learners. In this approach, an adaptation rule specification language and a user interface tool are provided to a content author or instructor to define adaptation rules. The defined rules are activated at different stages of processing the learning activities of an activity tree which models a composite learning object. System facilities are also provided for modeling the correlations among data conditions specified in adaptation rules using Bayesian Networks. Bayesian inference requires a prior distribution of a Bayesian model. This prior distribution is automatically derived by using the formulas presented in this paper together with prior probabilities and weights assigned by the content author or instructor. Each new learner‟s profile and performance data are used to update the prior distribution, which is then used to evaluate the next new learner. The system thus continues to improve the accuracy of learner evaluation as well as its adaptive capability. This approach enables an e-learning system to make proper adaptation decisions even though a learner‟s profile and performance data may be incomplete, inaccurate and/or contradictory