Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Introducing prerequisite relations in a multi-layered bayesian student model
UM'05 Proceedings of the 10th international conference on User Modeling
Interoperable Competencies Characterizing Learning Objects in Mathematics
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
ICLS '10 Proceedings of the 9th International Conference of the Learning Sciences - Volume 2
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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A standing question in the field of Intelligent Tutoring Systems and User Modeling in general is what is the appropriate level of model granularity (how many skills to model) and how is that granularity derived? In this paper we will explore models with varying levels of skill generality (1, 5, 39 and 106 skill models) and measure the accuracy of these models by predicting student performance within our tutoring system called ASSISTment as well as their performance on a state standardized test. We employ the use of Bayes nets to model user knowledge and to use for prediction of student responses. Our results show that the finer the granularity of the skill model, the better we can predict student performance for our online data. However, for the standardized test data we received, it was the 39 skill model that performed the best. We view this as support for fine-grained skill models despite the finest grain model not predicting the state test scores the best.