Identifiability: A Fundamental Problem of Student Modeling
UM '07 Proceedings of the 11th international conference on User Modeling
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Performance Factors Analysis --A New Alternative to Knowledge Tracing
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
A bayes net toolkit for student modeling in intelligent tutoring systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Ensembling predictions of student knowledge within intelligent tutoring systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Towards predicting future transfer of learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
The sum is greater than the parts: ensembling models of student knowledge in educational software
ACM SIGKDD Explorations Newsletter
Detecting learning moment-by-moment
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
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Student modeling is very important for ITS due to its ability to make inferences about latent student attributes. Although knowledge tracing (KT) is a well-established technique, the approach used to fit the model is still a major issue as different model-fitting approaches lead to different parameter estimates. Performance Factor Analysis, a competing approach, predicts student performance based on the item difficulty and student historical performances. In this study, we compared these two models in terms of their predictive accuracy and parameter plausibility. For the knowledge tracing model, we also examined different model fitting algorithms: Expectation Maximization (EM) and Brute Force (BF). Our results showed that KT+EM is better than KT+BF and comparable with PFA in predictive accuracy. We also examined whether the models' estimated parameter values were plausible. We found that by tweaking PFA, we were able to obtain more plausible parameters than with KT.