Cognitive modeling and intelligent tutoring
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The q-matrix method of fault-tolerant teaching in knowledge assessment and data mining
The q-matrix method of fault-tolerant teaching in knowledge assessment and data mining
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
A Student Model Based on Item Response Theory for TuTalk, a Tutorial Dialogue Agent
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Affect-aware tutors: recognising and responding to student affect
International Journal of Learning Technology
KT-IDEM: introducing item difficulty to the knowledge tracing model
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Probability estimation and a competence model for rule based e-tutoring systems
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
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Item Response Theory (IRT) models were investigated as a tool for student modeling in an intelligent tutoring system (ITS). The models were tested using real data of high school students using the Wayang Outpost, a computer-based tutor for the mathematics portion of the Scholastic Aptitude Test (SAT). A cross-validation framework was developed and three metrics to measure prediction accuracy were compared. The trained models predicted with 72% accuracy whether a student would answer a multiple choice problem correctly.