The knowledge tracing model has been widely used to model student knowledge and learning over time. It assumes that each skill has two knowledge parameters, prior and learn; and two performance parameters, slip and guess. The learn parameter represents the probability that a student will transition between the unlearned and the learned state after each question. The slip parameter is the probability that a student who understands a skill can make a careless mistake and the guess parameter is the probability a student may answer correctly in spite of not knowing the skill. When using the standard Knowledge Tracing (KT) model, it is assumed that the students’ probability of making the transition from the unlearned to the learned state is constant opportunities (or questions). Many researchers assume that student performance a minute later is the same as the next day. In the real world, coming into class on a new day may result in a student forgetting the material or a higher probability of them slipping. By taking this real world fact into consideration, this paper looks into how KT performs on each new day’s responses. A new day’s response is defined as a response that occurred on a later calendar date than the student’s previous response to a question of the same skill. KT’s new day error is far higher than same day error. A residual analysis showed that KT was largely over predicting student performance on each new day response. Based on the residual result, two hypotheses explain this phenomenon:

• 1. that students may forget between days and
• 2. that students may slip when answering the first question on a new day.

The slip hypothesis only affects the model’s prediction of new day events while the forget hypothesis could affect prediction of subsequent responses since it hypothesizes a change in the latent of knowledge. Two new models were developed based on Knowledge Tracing: a KT-Forget Model and a KT-Slip Model, where a new day variable is taken into account to affect either students’ knowledge or performance. To implement this, a new split-parameter KT model was introduced, which allowed learning a different forget parameter for new day opportunities than for same day but learn only a single learn rate parameter for each.