Abstract
This chapter focuses on connections between affect and cognition that are prevalent during deep learning. Deep learning occurs when a person attempts to comprehend difficult material, to solve a difficult problem, and to make a difficult decision. We emphasize theoretical perspectives that highlight the importance of cognitive disequilibrium to deep learning and problem solving. Cognitive disequilibrium occurs when there are obstacles to goals, interruptions of organized action sequences, impasses, system breakdowns, contradictions, anomalous events, dissonance, incongruities, negative feedback, uncertainty, and deviations from norms, and novelty. Cognitive disequilibrium launches a trajectory of cognitive and affective processes such as confusion and frustration until equilibrium is restored or disequilibrium is dampened via effortful problem solving and impasse resolution. We discuss the role of cognitive and task constraints in dictating the time-course of cognitive disequilibrium and affiliated affective states such as surprise, delight, confusion, and frustration. We conclude by discussing how these states and processes are mediated by self-concepts, goals, meta-knowledge, social interaction, and the learning environment.
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References
Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia. Journal of Educational Psychology, 96, 523–535.
Baker, R. S., D’Mello, S. K., Rodrigo, M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68, 223–241.
Barrett, L. (2006). Are emotions natural kinds? Perspectives on Psychological Science, 1, 28–58.
Barrett, L., Mesquita, B., Ochsner, K., & Gross, J. (2007). The experience of emotion. Annual Review of Psychology, 58, 373–403.
Berlyne, D. E. (1960). Conflict, arousal, and curiosity. New York: McGraw Hill.
Calvo, R. A., & D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 1–20.
Conati, C. (2002). Probabilistic assessment of user’s emotions in educational games. Applied Artificial Intelligence, 16(7–8), 555–575.
Craig, S., Graesser, A., Sullins, J., & Gholson, J. (2004). Affect and learning: An exploratory look into the role of affect in learning. Journal of Educational Media, 29, 241–250.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper and Row.
D’Mello, S. K., Craig, S. D., & Graesser, A. C. (2009). Multi-method assessment of affective experience and expression during deep learning. International Journal of Learning Technology, 4, 165–187.
D’Mello, S. K., Craig, S. D., & Graesser, A. C. (2009). Multi-method assessment of affective experience and expression during deep learning. International Journal of Learning Technology, 4, 165–187.
D’Mello, S., Craig, S., Witherspoon, A., McDaniel, B., & Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction, 18(1–2), 45–80.
D’Mello, S., Dowell, N., & Graesser, A. (2009). Cohesion relationships in tutorial dialogue as predictors of affective states. In V. Dimitrova, R. Mizoguchi, B. du Boulay, & A. Graesser (Eds.), Proceedings of 14th International Conference on Artificial Intelligence in Education (pp. 9–16). Amsterdam: IOS Press.
D’Mello, S., & Graesser, A. C. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-adapted Interaction, 20, 187.
D’Mello, S. K., & Graesser, A. C. (in press-a). Emotions during learning with AutoTutor. In P. Durlach and A. Lesgold (Eds.), Adaptive technologies for training and education. Cambridge: Cambridge University Press.
D’Mello, S., & Graesser, A. (in press-b). The half-life of cognitive-affective states during complex learning. Cognition and Emotion.
D’Mello, S., Taylor, R., & Graesser, A. (2007). Monitoring affective trajectories during complex learning. In D. McNamara & G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 203–208). Austin: Cognitive Science Society.
Davidson, R. J. (1998). Affective style and affective disorders: Perspectives from affective neuroscience. Cognition & Emotion, 12, 307–330.
Deci, E., & Ryan, R. (2002). The paradox of achievement: The harder you push, the worse it gets. In J. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 61–87). Orlando: Academic.
Dweck, C. (2002). Messages that motivate: How praise molds students’ beliefs, motivation, and performance (in surprising ways). In J. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 61–87). Orlando: Academic.
Ekman, P. (1984). Expression and the nature of emotion. In K. Scherer & P. Ekman (Eds.), Approaches to emotion (pp. 319–344). Hillsdale: Erlbaum.
Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3–4), 169–200.
Festinger, L. (1957). A theory of cognitive dissonance. Stanford: Stanford University Press.
Gee, J. P. (2003). What video games have to teach us about language and literacy. New York: Macmillan.
Graesser, A. C., & McNamara, D. S. (2010). Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist, 45, 234–244.
Graesser, A. C., D’Mello, S. K., Chipman, P., King, B., & McDaniel, B. (2007). Exploring relationships between affect and learning with AutoTutor. In R. Luckin, K. Koedinger, & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (pp. 16–23). Amsterdam: IOS Press.
Graesser, A. C., D’Mello, S. K., Craig, S. D., Witherspoon, A., Sullins, J., McDaniel, B., et al. (2008). The relationship between affect states and dialogue patterns during interactions with AutoTutor. Journal of Interactive Learning Research, 19, 293–312.
Graesser, A. C., D’Mello, S., & Person, N. K. (2009). Metaknowledge in tutoring. In D. Hacker, J. Donlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 361–382). New York: Taylor & Francis.
Graesser, A. C., Jackson, G. T., & McDaniel, B. (2007). AutoTutor holds conversations with learners that are responsive to their cognitive and emotional states. Educational Technology, 47, 19–22.
Graesser, A., Lu, S., Olde, B., Cooper-Pye, E., & Whitten, S. (2005). Question asking and eye tracking during cognitive disequilibrium: Comprehending illustrated texts on devices when the devices break down. Memory and Cognition, 33, 1235–1247.
Graesser, A. C., Ozuru, Y., & Sullins, J. (2009). What is a good question? In M. McKeown (Ed.), Festscrift for Isabel Beck. Mahwah: Erlbaum.
Graesser, A. C., & Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31, 104–137.
Jackson, G. T., & Graesser, A. C. (2007). Content matters: An investigation of feedback categories within an ITS. In R. Luckin, K. Koedinger, & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (pp. 127–134). Amsterdam: IOS Press.
Johnson, W. L., & Valente, A. (2008). Tactical language and culture training systems: Using artificial intelligence to teach foreign languages and cultures. In Proceedings of the 20th Innovative Applications of Artificial Intelligence (IAAI) Conference. Los Angeles: Alelo.
Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207, 203–208.
Lazarus, R. (1991). Emotion and adaptation. New York: Oxford University Press.
Lazarus, R. (2000). The cognition-emotion debate: A bit of history. In M. Lewis & J. Haviland-Jones (Eds.), Handbook of emotions (pp. 1–20). New York: Guilford Press.
Lehman, B., Matthews, M., D’Mello, S., & Person, N. (2008). What are you feeling? Investigating student affective states during expert human tutoring sessions. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the 9th international conference on Intelligent Tutoring Systems (pp. 50–59). Berlin: Springer.
Lepper, M., & Woolverton, M. (2002). The wisdom of practice: Lessons learned from the study of highly effective tutors. In J. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 135–158). Orlando: Academic.
Lewis, M., Haviland-Jones, J., & Barrett, L. (Eds.). (2008). Handbook of emotions (3rd ed.). New York: Guilford Press.
Maki, R. H. (1998). Text predictions over text material: Metacognition in educational theory and practice. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 117–144). Mahwah: Lawrence Erlbaum Associates Publisher.
Mandler, G. (1976). Mind and emotion. New York: Wiley.
Mandler, G. (1984). Mind and body: The psychology of emotion and stress. New York: W.W. Norton & Company.
McCrudden, M. T., & Schraw, G. (2007). Relevance and goal-focusing in text processing. Educational Psychology Review, 19, 113–139.
Meyer, D., & Turner, J. (2006). Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review, 18(4), 377–390.
Miyake, N., & Norman, D. A. (1979). To ask a question, one must know enough to know what is not known. Journal of Verbal Learning and Verbal Behavior, 18(3), 357–364.
Ortony, A., Clore, G., & Collins, A. (1988). The cognitive structure of emotions. New York: Cambridge University Press.
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.
Piaget, J. (1952). The origins of intelligence. New York: International University Press.
Picard, R. (1997). Affective computing. Boston: MIT Press.
Rosenberg, E. (1998). Levels of analysis and the organization of affect. Review of General Psychology, 2(3), 247–270.
Russell, J. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110, 145–172.
Scherer, K. R. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition and Emotion, 23, 1307–1351.
Schutz, P. A., & Pekrun, R. (Eds.). (2007). Emotion in education. San Diego: Academic.
Shaffer, D. W., & Graesser, A. (2010). Using a quantitative model of participation in a community of practice to direct automated mentoring in an ill-defined domain. Workshop at Intelligent Tutoring Systems (ITS), Pittsburgh, PA.
Silvia, P. J. (2009). Looking past pleasure: Anger, confusion, disgust, pride, surprise, and other unusual aesthetic emotions. Psychology of Aesthetics Creativity and the Arts, 3(1), 48–51.
Stein, N., Hernandez, M., & Trabasso, T. (2008). Advances in modeling emotions and thought: The importance of developmental, online, and multilevel analysis. In M. Lewis, J. M. Haviland-Jones, & L. F. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 574–586). New York: Guilford Press.
VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31, 3–62.
Weiner, B. (1986). An attributional theory of motivation and emotion. New York: Springer.
Zajonc, R. (1984). On the primacy of affect. American Psychologist, 39, 117–123.
Acknowledgments
The research on was supported by the National Science Foundation (ITR 0325428, ALT-0834847, DRK-12-0918409), and the Institute of Education Sciences (R305H050169, R305B070349, R305A080589, R305A080594). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF or IES.
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Graesser, A., D’Mello, S.K. (2011). Theoretical Perspectives on Affect and Deep Learning. In: Calvo, R., D'Mello, S. (eds) New Perspectives on Affect and Learning Technologies. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9625-1_2
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