When using the Knowledge Tracing model, it is assumed that the student’s probability of making the transition from the unlearned to the learned state is not changing across opportunities, while in the real world students may forget the previously learned knowledge when coming into class on a new day. This fact assumes that there is a great possibility that a student’s forgetting rate is not zero. Poor performance on a new day may also suggest that students may not actually be “forgetting” but instead, they might just be “slipping.” The researchers used Bayesian networks and Expectation Maximization to detect whether time had any influence on the forget parameter and the slip parameter of the KT model. The model with the better predictive accuracy will indicate the better cognitive explanation of the data.