ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Blending Assessment and Instructional Assisting
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
What Do Academic Users Really Want from an Adaptive Learning System?
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Modeling individualization in a bayesian networks implementation of knowledge tracing
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
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Student modeling plays an important role in educational research. Many techniques have been developed focusing on accurately estimating student performances. In this paper, using Performance Factors Analysis as our framework, we examine what components of the model enable us to better predict, and consequently better understand, student performance. Using transfer models to predict is very common across different student modeling techniques, as student proficiencies on those required skills are believed, to a large degree, to determine student performance. However, we found that problem difficulty is an even more important predictor than student knowledge of the required skills. In addition, we found that using student proficiencies across all skills works better than just using those skills thought relevant by the transfer model. We tested our proposed models with two transfer models of fine- and coarse-grain sizes; the results suggest that the improvement is not simply an illusion due to possible mistakes in associating skills with problems.