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Using Deep Reinforcement Learning to Train and Evaluate Instructional Sequencing Policies for an Intelligent Tutoring System
STEP leverages the student model by representing the student’s knowledge state as a probability vector of knowing each skill and using the student’s estimated learning gains as its reward function to evaluate candidate policies.
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