Dynamics of affective states during complex learning

We propose a model to explain the dynamics of affective states that emerge during deep learning activities. The model predicts that learners in a state of engagement/flow will experience cognitive disequilibrium and confusion when they face contradictions, incongruities, anomalies, obstacles to goals, and other impasses. Learners revert into the engaged/flow state if equilibrium is restored through thought, reflection, and problem solving. However, failure to restore equilibrium as well as obstacles that block goals trigger frustration, which, if unresolved, will eventually lead to boredom. The major hypotheses of the model were supported in two studies in which participants completed a 32–35 min tutoring session with a computer tutor. Their affective states were tracked at approximately 110 points in their tutoring sessions via a retrospective affect judgment protocol. Time series analyses confirmed the presence of confusion–engagement/flow, boredom–frustration, and confusion–frustration oscillations. We discuss enhancements of the model to address individual differences and pedagogical and motivational strategies that are inspired by the model.