This paper makes two contributions. First, they showed assumptions made in Knowledge Tracing model, that students don’t forget, is false. While this might not be terribly surprising, they identify a particular situation in which the standard KT model has systematic errors in predicting student performance, which is on new day responses. Secondly, they present a model to account for this phenomenon which does a reliably better job of fitting student data in some datasets. This is significant as KT has proved itself to be a very effective model, difficult to improve upon. It is also noteworthy that KT is easily interpretable, and it is beneficial to be able to have a clean model that fits easily into the Bayesian framework and inherits this interpretability. Their contribution is that researchers should pay attention to “time” and we have demonstrated a method that takes this into account and improves modeling performance.