Abstract
One of the most popular methods for modeling students' knowledge is Corbett and Anderson's[1] Bayesian Knowledge Tracing (KT) model. The original Knowledge Tracing model does not allow for individualization. Recently, Pardos and Heffernan [4] 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
