Inferring learning and attitudes from a Bayesian Network of log file data

A student's goals and attitudes while interacting with a tutor are typically unseen and unknowable. However their outward behavior (e.g. problem-solving time, mistakes and help requests) is easily recorded and can reflect hidden affect status. This research evaluates the accuracy of a Bayesian Network to infer a student's hidden attitude toward learning, amount learned and perception of the system from log-data. The long term goal is to develop tutors that self-improve their student models and their teaching, dynamically can adapt pedagogical decisions about hints and help improve student's affective, intellectual and learning situation based on inferences about their goals and attitude.