Unified theories of cognition
The Architecture of Cognition
Designing Interactive Speech Systems: From First Ideas to User Testing
Designing Interactive Speech Systems: From First Ideas to User Testing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Proceedings of the 9th international conference on Intelligent Tutoring Systems
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
The Interaction Plateau: Answer-Based Tutoring
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Dialogue Modes in Expert Tutoring
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Proceedings of the 9th international conference on Intelligent Tutoring Systems
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems
Knowledge-Based Systems
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Tools for Authoring a Dialogue Agent that Participates in Learning Studies
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Comparing Linguistic Features for Modeling Learning in Computer Tutoring
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Out of the Lab and into the Classroom: An Evaluation of Reflective Dialogue in Andes
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Journal of Artificial Intelligence Research
To Elicit Or To Tell: Does It Matter?
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Scaling POMDPs for Spoken Dialog Management
IEEE Transactions on Audio, Speech, and Language Processing
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Pedagogical strategies are policies for a tutor to decide the next action when there are multiple actions available. When the content is controlled to be the same across experimental conditions, there has been little evidence that tutorial decisions have an impact on students' learning. In this paper, we applied Reinforcement Learning (RL) to induce two sets of pedagogical policies from pre-existing human interaction data. The NormGain set was derived with the goal of enhancing tutorial decisions that contribute to learning while the InvNormGain set was derived with the goal of enhancing those decisions that contribute less or even nothing to learning. The two sets were then tested with human students. Our results show that when the content was controlled to be the same, different pedagogical policies did make a difference in learning and more specifically, the NormGain students outperformed their peers. Overall our results suggest that content exposure and practice opportunities can help students to learn even when tutors have poor pedagogical tutorial tactics. However, with effective tutorial tactics, students can learn even more.