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Educational data mining
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Theory and Practice: Improving Retention Performance through Student Modeling and System Building
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A Prediction Model Uses the Sequence of Attempts and Hints to Better Predict Knowledge: Better to Attempt the Problem First, Rather Than Ask for A Hint
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Student Modeling within a Computer Tutor for Mathematics: Using Bayesian Networks and Tabling Methods
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Tracing Knowledge and Engagement in Parallel by Observing Behavior in Intelligent Tutoring Systems
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Modeling Student Retention in an Environment with Delayed Testing
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Real-time Assessment, Prediction, and Scaffolding of Middle School Students’ Data Collection Skills within Physical Science Simulations
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Predictive Models of Student Learning
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Reaching More Students: A Web-based Intelligent Tutoring System with support for Offline Access
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Responding to Moments of Learning
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Learning the Effectiveness of Content and Methodology in an Intelligent Tutoring System
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