title: The half-life of cognitive-affective states during complex learning.
abstract: We investigated the temporal dynamics of students' cognitive-affective states (confusion, frustration, boredom, engagement/flow, delight, and surprise) during deep learning activities. After a learning session with an intelligent tutoring system with conversational dialogue, the cognitive-affective states of the learner were classified by the learner, a peer, and two trained judges at approximately 100 points in the tutorial session. Decay rates for the cognitive-affective states were estimated by fitting exponential curves to time series of affect responses. The results partially confirmed predictions of goal-appraisal theories of emotion by supporting a tripartite classification of the states along a temporal dimension: persistent states (boredom, engagement/flow, and confusion), transitory states (delight and surprise), and an intermediate state (frustration). Patterns of decay rates were generally consistent across affect judges, except that a reversed actor-observer effect was discovered for engagement/flow and frustration. Correlations between decay rates of the cognitive-affective states and several learning measures confirmed the major predictions and uncovered some novel findings that have implications for theories of pedagogy that integrate cognition and affect during deep learning.
authors.affiliation: Department of Psychology, University of Memphis, Memphis, TN 38152, USA. sdmello@memphis.edu
authors.forename: Sidney
authors.initials: S
authors.lastname: D'Mello
authors.forename: Art
authors.initials: A
authors.lastname: Graesser
mesh_terms: D000339:Affect; D003071:Cognition; D006801:Humans; D007858:Learning; D013997:Time Factors