Date of Original Version
Abstract or Description
Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from one day of interaction and tested with data from a later day. In terms of predicting what state a student was in during any 2 second period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. Further, the prediction accuracy using combined cognitive model and fMRI signal showed superadditivity of accuracies when using either cognitive model or fMRI signal alone
Proceedings of the Third International Conference on Educational Data Mining (EDM2010).