Modeling Cognitive-Affective Dynamics with Hidden Markov Models

We present and test a theory of cognitive disequilibrium to explain the dynamics of the cognitive-affective states that emerge during deep learning activities. The theory postulates an important role for cognitive disequilibrium, a state that occurs when learners face obstacles to goals, contradictions, incongruities, anomalies, uncertainty, and salient contrasts. The major hypotheses of the theory were supported in two studies in which participants completed a tutoring session with a computer tutor after which they provide judgments on their cognitive-affective states via a retrospective judgment protocol. Hidden Markov Models constructed from time series of learners' cognitive-affective states confirmed the major predictions as well as suggested refinements for the theory of cognitive disequilibrium during deep learning.