Deriving prior distributions for Bayesian models used to achieve adaptive e-learning
Deriving prior distributions for Bayesian models used to achieve adaptive e-learning
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