University of Pittsburgh

Decomposing Response Time to Give Better Predictions of Student Performance

Graduate Student
Friday, November 8, 2019 - 1:00pm - 1:30pm


In educational systems, response time is defined as the time between when students see a problem step and when they first reacted to this step. Response time has been used as an important predictor of student performance in various kinds of models. Much of this work is based on the hypothesis that if a student responds to a 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 be cycling through different kinds of states within a single response time and the time spent in those states could have separate effects on students’ performance. We hypothesize that identifying the different states and estimating how much time is devoted to them in a single response time period will help us give more accurate predictions of student performance. In this talk, I present our methods to decompose response time into meaningful sub-categories in a reading application, and a predictive model that uses the derived sub-categories of response time as predictors instead of raw response time.

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