Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Limitations of Student Control: Do Students Know When They Need Help?
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
An evaluation of decision-theoretic tutorial action selection
An evaluation of decision-theoretic tutorial action selection
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Factors influencing the performance of Dynamic Decision Network for INQPRO
Computers & Education
What Level of Tutor Interaction is Best?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Hints: is it better to give or wait to be asked?
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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DT Tutor (DT), an ITS that uses decision theory to select tutorial actions, was compared with both a Fixed-Policy Tutor (FT) and a Random Tutor (RT). The tutors were identical except for the method they used to select tutorial actions: FT employed a common fixed policy while RT selected randomly from relevant actions. This was the first comparison of a decision-theoretic tutor with a non-trivial competitor (FT). In a two-phase study, first DT's probabilities were learned from a training set of student interactions with RT. Then a panel of judges rated the actions that RT took along with the actions that DT and FT would have taken in identical situations. DT was rated higher than RT and also higher than FT both overall and for all subsets of scenarios except help requests, for which DT's and FT's ratings were equivalent.