A model for reasoning about persistence and causation
Computational Intelligence
ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Relating Machine Estimates of Students' Learning Goals to Learning Outcomes: A DBN Approach
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
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Students interacted with an intelligent tutoring system to learn grammatical rules for an artificial language. Six tutoring policies were explored. One, based on a Dynamic Bayes' Network model of skills, was learned from the performance of previous students. Overall, this policy and other intelligent policies outperformed random policies. Some policies allowed students to choose one of three problems to work on, while others presented a single problem at each iteration. The benefit of choice was not apparent in group statistics; however, there was a strong interaction with gender. Overall, women learned less than men, but they learned different amounts in the choice and no choice conditions, whereas men seemed unaffected by choice. We explore reasons for these interactions between gender, choice and learning.