Yutao Wang, Neil T. Heffernan
One of the most popular methods for modeling students’ knowledge is Corbett and Anderson’s Bayesian Knowledge Tracing (KT) model. The original Knowledge Tracing model does not allow for individualization. Recently, Pardos and Heffernan  showed that more information about students’ prior knowledge can help build a better fitting model and provide a more accurate prediction of student data. Our goal was to further explore the individualization of student parameters in order to allow the Bayesian network to keep track of each of the four parameters per student: prior knowledge, guess, slip and learning. We proposed a new Bayesian network model called the Student Skill model (SS), and evaluated it in comparison with the traditional knowledge tracing model in both simulated and realword experiments. The new model predicts student responses better than the standard knowledge tracing model when the number of students and the number of skills are large.
The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_51.