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Boredom and student modeling in intelligent tutoring systems
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Student Modeling From Different Aspects
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Can a computer adaptive assessment system determine, better than traditional methods, whether students know mathematics skills?
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Learning the Effectiveness of Content and Methodology in an Intelligent Tutoring System
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Evaluating Predictions of Transfer and Analyzing Student Motivation
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Towards Personalized Learning using Counterfactual Inference for Randomized Controlled Trials
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Examining Student Effort on Hint through Response Time Decomposition
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Developing a Cognitive Rule-Based Tutor for the ASSISTment System
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Developing an Affordable Authoring Tool For Intelligent Tutoring Systems
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Improving Feedback Recommendation in Intelligent Tutoring Systems using NLP
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Measuring Student Engagement in an Intelligent Tutoring System
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Towards Assessing Students' Fine Grained Knowledge: Using an Intelligent Tutor for Assessment
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Behavior Detectors to Support Feedback Generation using Problem-Solving Action Data
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Collaborative Warrior Tutoring
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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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The Common Tutor Object Platform
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Reaching More Students: A Web-based Intelligent Tutoring System with support for Offline Access
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Layout Optimization for Distributed Relational Databases Using Machine Learning
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Leveraging Influential Factors into Bayesian Knowledge Tracing
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