An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Off-task behavior in the cognitive tutor classroom: when students "game the system"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Diagnosing and acting on student affect: the tutor's perspective
User Modeling and User-Adapted Interaction
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper discusses the data-driven development of a model which predicts whether a student could answer a question correctly without requesting help. This model contributes to a broader piece of research, the primary goal of which was to predict affective characteristics of students working in ILEs. The paper presents the bayesian network which provides adequate predictions, and discusses how its accuracy is taken into account when the model is integrated in an ILE. Future steps to improve the results are briefly discussed.