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Simulated Students, Mastery Learning, and Improved Learning Curves for Real-World Cognitive Tutors

By Stephen E. Fancsali, Tristan Nixon, Annalies Vuong and Steven Ritter

Abstract

Abstract. We briefly describe three approaches to simulating students to develop and improve intelligent tutoring systems. We review recent work with simulated student data based on simple probabilistic models that provides important insight into practical decisions made in the deployment of Cognitive Tutor software, focusing specifically on aspects of mastery learning in Bayesian Knowledge Tracing and learning curve analysis to improve cognitive (skill) models. We provide a new simulation approach that builds on earlier efforts to better visualize aggregate learning curves

Topics: Knowledge tracing, learning curves, student modeling, Cognitive Tutor
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.415.9903
Provided by: CiteSeerX
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