Online learning algorithm for learner knowledge model
Jianwen Xie · Journal of Computer Applications · 2012
Learner knowledge model is the foundation of the teaching process and strategy on Intelligent Tutoring Systems(ITS).Because of the uncertainty of recognizing knowledge level of learner and the real-time changes of knowledge level,it is very difficult to construct a model to reflect learner's knowledge level and its change correctly.The paper used Bayesian network for learner knowledge modeling.According to knowledge level's change during learners' learning process,problem knot was introduced into knowledge model,and Voting EM algorithm was used for online learning and updating of knowledge model's parameters.Finally,the paper introduced confidence factor and time updating mark to improve the efficiency of online parameters learning and revise the result.The experimental results indicate that the model can reflect learner's knowledge status better,and can quickly keep up with the change of knowledge level.It can help ITS to evaluate the learning effects better.