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
Decision Analysis
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
A decision-theoretic approach to scientific inquiry exploratory learning environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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Providing pedagogical interventions under uncertainty has always been a challenge for decision-theoretic Intelligent Tutoring Systems (ITSs). Studies have shown that apart from the challenges in structuring the domain problem, the issue of how and when to intervene contributes to the usefulness and overall performance of a decision-theoretic ITS. In this study, two Dynamic Decision Network (DDN) models were proposed: 'intervening' and 'non-intervening'. Field tests, which involved 31 learners and 6 domain experts, were conducted to identify the models' classification accuracies. The empirical results showed that the 'non-intervening' outperformed the 'intervening', implying that the 'non-intervening' approach for designing pedagogical interventions is more appropriate for INQPRO, a scientific inquiry learning environment developed within this research work.