Modeling Cognitive-Affective Dynamics with Hidden Markov Models

Abstract

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.