GI '05 Proceedings of Graphics Interface 2005
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
User modeling: a notoriously black art
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
Using contextual factors analysis to explain transfer of least common multiple skills
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Contextual slip and prediction of student performance after use of an intelligent tutor
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Modeling individualization in a bayesian networks implementation of knowledge tracing
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Detecting the moment of learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
The sum is greater than the parts: ensembling models of student knowledge in educational software
ACM SIGKDD Explorations Newsletter
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
WEBsistments: enabling an intelligent tutoring system to excel at explaining rather than coaching
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Towards automatically detecting whether student learning is shallow
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
WTF? detecting students who are conducting inquiry without thinking fastidiously
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
User Modeling and User-Adapted Interaction
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Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used to compute latencies of actions. On the other hand, existing models used for educational data mining (e.g. Learning Factors Analysis (LFA)[2]) and model search do not tend to allow the creation of a “model overlay” that traces predictions for individual students with individual skills so as to allow the adaptive instruction to automatically remediate performance. Because these limitations make the transition from model search to model application in adaptive instruction more difficult, this paper describes our work to modify an existing data mining model so that it can also be used to select practice adaptively. We compare this new adaptive data mining model (PFA, Performance Factors Analysis) with two versions of LFA and then compare PFA with standard KT.