The transfer of cognitive skill
The transfer of cognitive skill
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor
International Journal of Artificial Intelligence in Education
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
How diagram interaction supports learning: evidence from think alouds during intelligent tutoring
Diagrams'10 Proceedings of the 6th international conference on Diagrammatic representation and inference
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
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
Towards automatically detecting whether student learning is shallow
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Proceedings of the Third International Conference on Learning Analytics and Knowledge
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We present an automated detector that can predict a student's future performance on a transfer post-test, a post-test involving related but different skills than the skills studied in the tutoring system, within an Intelligent Tutoring System for College Genetics. We show that this detector predicts transfer better than Bayesian Knowledge Tracing, a measure of student learning in intelligent tutors that has been shown to predict performance on paper post-tests of the same skills studied in the intelligent tutor. We also find that this detector only needs limited amounts of student data (the first 20% of a student's data from a tutor lesson) in order to reach near-asymptotic predictive power.