Deeper Knowledge Tracing by Modeling Skill Application Context for Better Personalized Learning
Yun Huang · 2016
Traditional Knowledge Tracing, which traces students' knowledge of each decomposed individual skill, has been a popular learner model for adaptive tutoring. Typically, a student is guided to the next skill when the student's knowledge on current skill is inferred as mastery. Unfortunately, this traditional approach no longer suffices to model complex skill practices where simple decompositions can not capture potential additional skills underlying the context as a whole. In such cases, mastery should only be granted when a student not only understands the basic of a skill but also can fluently apply a skill in varied application contexts. In this thesis, we aim to propose a data-driven approach to construct learner models considering different skill application contexts for tracing deeper knowledge, primarily based on Bayesian Networks. We aim to conduct novel, comprehensive, ``deep" evaluations, including internal data-drive evaluations, and external end-user evaluations examining the real world impact for students' personalized learning.