The half-life of cognitive-affective states during complex learning
- 3 March 2011
- journal article
- research article
- Published by Taylor & Francis Ltd in Cognition and Emotion
- Vol. 25 (7), 1299-1308
- https://doi.org/10.1080/02699931.2011.613668
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.Keywords
This publication has 15 references indexed in Scilit:
- Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environmentsInternational Journal of Human-Computer Studies, 2010
- Multimethod assessment of affective experience and expression during deep learningInternational Journal of Learning Technology, 2009
- Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive–affective states during learningCognition and Emotion, 2008
- We Feel, Therefore We Learn: The Relevance of Affective and Social Neuroscience to EducationMind, Brain, and Education, 2007
- Re-conceptualizing Emotion and Motivation to Learn in Classroom ContextsEducational Psychology Review, 2006
- Achievement goals and discrete achievement emotions: A theoretical model and prospective test.Journal of Educational Psychology, 2006
- Individual differences in rate of affect change: Studies in affective chronometry.Journal of Personality and Social Psychology, 2003
- Mixed-Effects Models in Sand S-PLUSPublished by Springer Science and Business Media LLC ,2000
- Levels of Analysis and the Organization of AffectReview of General Psychology, 1998
- Affective Style and Affective Disorders: Perspectives from Affective NeuroscienceCognition and Emotion, 1998