Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
Models of attention in computing and communication: from principles to applications
Communications of the ACM
Student Strategies for Learning Programming from a Computational Environment
ITS '92 Proceedings of the Second International Conference on Intelligent Tutoring Systems
Automated Advice-Giving Strategies for Scientific Inquiry
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Interactivities in Music Intelligent Tutoring System
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Classifying learner engagement through integration of multiple data sources
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Detecting when students game the system, across tutor subjects and classroom cohorts
UM'05 Proceedings of the 10th international conference on User Modeling
A decision-theoretic approach to scientific inquiry exploratory learning environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
A comparison of decision-theoretic, fixed-policy and random tutorial action selection
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Coaching within a domain independent inquiry environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
User behaviour-driven group formation through case-based reasoning and clustering
Expert Systems with Applications: An International Journal
Evidence conflict analysis approach to obtain an optimal feature set for bayesian tutoring systems
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
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There has been an increasing interest in employing decision-theoretic framework for learner modeling and provision of pedagogical support in Intelligent Tutoring Systems (ITSs). Much of the existing learner modeling research work focuses on identifying appropriate learner properties. Little attention, however, has been given to leverage Dynamic Decision Network (DDN) as a dynamic learner model to reason and intervene across time. Employing a DDN-based learner model in a scientific inquiry learning environment, however, remains at infant stage because there are factors contributed to the performance the learner model. Three factors have been identified to influence the matching accuracy of INQPRO's learner model. These factors are thestructureof DDN model, thevariable instantiationapproach, and theweightsassignmentmethodfortwoconsecutiveDecisionNetworks (DNs). In this research work, a two-phase empirical study involving 107 learners and six domain experts was conducted to determine the optimal conditions for the INQPRO's dynamic learner model. The empirical results suggested each time-slice of the INQPRO's DDN should consist of a DN, and that DN should correspond to the Graphical User Interface (GUI) accessed. In light of evidence, observable variables should be instantiated to their observedstates; leaving the remaining observable nodes uninstantiated. The empirical results also indicated that varying weights between two consecutive DNs could optimize the matching accuracy of INQPRO's dynamic learner model.