Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
The Dynamics of Affective Transitions in Simulation Problem-Solving Environments
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Towards Emotionally-Intelligent Pedagogical Agents
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
International Journal of Artificial Intelligence in Education
International Journal of Artificial Intelligence in Education
Bayesian networks: A teacher's view
International Journal of Approximate Reasoning
Affect and Usage Choices in Simulation Problem-Solving Environments
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Solving POMDPs with continuous or large discrete observation spaces
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Expert Systems with Applications: An International Journal
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Contextual slip and prediction of student performance after use of an intelligent tutor
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Improving intelligent tutoring systems: using expectation maximization to learn student skill levels
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Hidden state and reinforcement learning with instance-based stateidentification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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With Partially Observable Markov Decision Processes (POMDPs), Intelligent Tutoring Systems (ITSs) can model individual learners from limited evidence and plan ahead despite uncertainty. However, POMDPs need appropriate representations to become tractable in ITSs that model many learner features, such as mastery of individual skills or the presence of specific misconceptions. This article describes two POMDP representations—state queues and observation chains—that take advantage of ITS task properties and let POMDPs scale to represent over 100 independent learner features. A real-world military training problem is given as one example. A human study (n = 14) provides initial validation for the model construction. Finally, evaluating the experimental representations with simulated students helps predict their impact on ITS performance. The compressed representations can model a wide range of simulated problems with instructional efficacy equal to lossless representations. With improved tractability, POMDP ITSs can accommodate more numerous or more detailed learner states and inputs.