In this work they attempt to model the time factor to better predict students’ learning performance in intelligent tutoring systems. Due to their experiment results that new KTforget model worked very well on Cognitive Tutor datasets while failed on ASSISTments Tutor datasets, they need to further investigate into the real reasons which caused this. Thus, they would like to know when using KT-forget model is not beneficial. In this paper they only made two assumptions that the parameters “forget” and “slip” will be affected by time factor. They have not yet looked into the performance of other parameters that might be affected by time, for example: students may have a fresh mind and learn more on a new day, which means a new parameter “learn new day” should be modeled. Also, it is possible that “time” should connect to these two parameters at once. They will keep on delving into these possibilities to see whether further improvement incorporating time can be obtained. If this is achieved in the future, They can build an ensemble model that combines regular KT’s results on the same day with the new model’s results on a new day. Their work only focuses on whether students answer the questions in one day or in a new day, they do not pay attention to the intervals between the same day and a new day. Pavlik and Anderson’s study showed that longer intervals should have a greater impact more on students’ performance while shorter intervals may have very little effect on actual responses. These topics deserve further investigation to figure out how to leverage the valuable time information and build better user models.