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Response time has been used as an important predictor of student performance in various models. Much of this work is based on the hypothesis that if students respond to a problem step too quickly or too slowly, they are most likely to be unsuccessful in that step. However, something that is less explored is that students may cycle through different states within a single response time and the time spent in those states may have separate effects on students’ performance. The core hypothesis of this work is that identifying the different states and estimating how much time is devoted to them in a single response time period will help us predict student performance more accurately. In this work, we de-compose response time into meaningful subcategories that can be indicative of helpful or harmful cognitive states. We then show how a model that is using these subcategories as predictors instead of response time as a whole outperforms both a linear and a non-linear baseline model.