Advertisement

Significant Accomplishments, New Challenges, and New Perspectives

  • Sidney K. D’MelloEmail author
  • Rafael A. Calvo
Chapter
Part of the Explorations in the Learning Sciences, Instructional Systems and Performance Technologies book series (LSIS, volume 3)

Abstract

This concluding chapter provides an integrative summative evaluation of the various threads of interdisciplinary research described in this book. After reflecting on the recent emphasis on emotions in seemingly disparate fields such as cognitive psychology, computer science, and education, we synthesize some of the important milestones achieved in the still nascent field of affect-sensitive learning technologies. These defining accomplishments include (a) an infusion of theories on emotions and learning, (b) the identification of affective states that are relevant to learning along with some of their antecedents and consequents, (c) the advance of automated affect detection systems, and (d) the emergence of some of the first fully automated affect-sensitive learning environments. Next, we highlight some of the open problems and promising areas for future research. These include (a) obtaining coherence among multiple levels of analysis, (b) modeling complex interactions between affective traits, moods, affect-elicitation events, and emotions, (c) incorporating temporal dependencies and affective dynamics into models of emotion, (d) reconceptualizing existing affect detection systems, (e) revisiting reactive emotion regulation strategies, (f) the need for proactive emotionally intelligent strategies, and (g) the importance of broadening the scope of affect and learning research so that next-generation learning technologies are consistent with the learning styles of the twenty-first century and beyond.

Keywords

Affective State Basic Emotion Social Emotion Emotion Theory Affective Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Sidney D’Mello was supported by the National Science Foundation (ITR 0325428, HCC 0834847) and the Institute of Education Sciences (R305A080594). Any opinions, findings, and conclusions, or recommendations expressed in this chapter are those of the authors and do not necessarily reflect the views of NSF and IES.

References

  1. Afzal, S., & Robinson, P. (2009). Natural affect data – collection & annotation in a learning context. Paper presented at the Proceedings of 2009 International Conference on Affective Computing & Intelligent Interaction, Amsterdam.Google Scholar
  2. Ainley, M. (2008). Interest: A significant thread binding cognition and affect in the regulation of learning. International Journal of Psychology, 43(3–4), 17–18.Google Scholar
  3. Alexander, P. A., & Jetton, T. L. (1996). The role of importance and interest in the processing of text. Educational Psychology Review, 8(1), 89–121.CrossRefGoogle Scholar
  4. Arnold, M. B. (1960). Emotion and personality. New York: Columbia University Press.Google Scholar
  5. Arroyo, I., Woolf, B., Cooper, D., Burleson, W., Muldner, K., & Christopherson, R. (2009). Emotion sensors go to school. In V. Dimitrova, R. Mizoguchi, B. Du Boulay, & A. Graesser (Eds.), Proceedings of 14th International Conference on Artificial Intelligence In Education (pp. 17–24). Amsterdam: IOS Press.Google Scholar
  6. Averill, J. R. (1980). A constructivist view of emotion. In R. Plutchik & H. Kellerman (Eds.), Emotion: Theory, research and experience: Vol. I. Theories of emotion (pp. 305–339). New York: Academic.Google Scholar
  7. Azevedo, R., & Strain, A. C. (2011). Integrating cognitive, metacognitive, and affective regulatory processes with MetaTutor. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  8. Barrett, L. (2006). Are emotions natural kinds? Perspectives on Psychological Science, 1, 28–58.CrossRefGoogle Scholar
  9. Barrett, L. F. (2009). Variety is the spice of life: A psychological construction approach to understanding variability in emotion. Cognition & Emotion, 23(7), 1284–1306.CrossRefGoogle Scholar
  10. Barrett, L., Mesquita, B., Ochsner, K., & Gross, J. (2007). The experience of emotion. Annual Review of Psychology, 58, 373–403.CrossRefGoogle Scholar
  11. Bjork, R. A., & Linn, M. C. (2006). The science of learning and the learning of science: Introducing desirable difficulties. American Psychological Society Observer, 19, 3.Google Scholar
  12. Boehner, K., DePaula, R., Dourish, P., & Sengers, P. (2007). How emotion is made and measured. International Journal of Human-Computer Studies, 65(4), 275–291.Google Scholar
  13. Bower, G. (1992). How might emotions affect learning. In S. A. Christianson (Ed.), The handbook of emotion and memory: Research and theory (pp. 3–31). Hillsdale: Erlbaum.Google Scholar
  14. Burleson, W. (2011). Advancing a multi-modal real-time affective sensing research platform. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  15. 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), 18–37.CrossRefGoogle Scholar
  16. Calvo, R., & D’Mello, S. (Eds.). (in preparation). New perspectives on affect and learning technologies. New York: Springer.Google Scholar
  17. Camras, L. A., & Witherington, D. C. (2005). Dynamical systems approaches to emotional development. Developmental Review, 25(3–4), 328–350.CrossRefGoogle Scholar
  18. Caridakis, G., Malatesta, L., Kessous, L., Amir, N., Paouzaiou, A., & Karpouzis, K. (2006). Modeling naturalistic affective states via facial and vocal expression recognition. Paper presented at the International Conference on Multimidal Interfaces. Banff, Canada.Google Scholar
  19. Chen, L., Huang, T., Miyasato, T., & Nakatsu, R. (1998). Multimodal human emotion/expression recognition. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (pp. 366–371). Washington, DC: IEEE Computer Society.Google Scholar
  20. Clifford, M. (1988). Failure tolerance and academic risk-taking in ten- to twelve-year-old students. British Journal of Educational Psychology, 58, 15–27.CrossRefGoogle Scholar
  21. Coan, J. A. (2010). Emergent ghosts of the emotion machine. Emotion Review, 2(3), 274–285.CrossRefGoogle Scholar
  22. Conati, C. (2011). Combining cognitive appraisal and sensors for affect detection in a framework for modeling user affect. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  23. Cooper, D. G., Arroyo, I., & Woolf, B. P. (2011). Actionable affective processing for automatic tutor interventions. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  24. Craig, S., D’Mello, S., Witherspoon, A., & Graesser, A. (2008). Emote aloud during learning with AutoTutor: Applying the facial action coding system to cognitive-affective states during learning. Cognition & Emotion, 22(5), 777–788.CrossRefGoogle Scholar
  25. Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass.Google Scholar
  26. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper and Row.Google Scholar
  27. D’Mello, S., & Graesser, A. (2010). Modeling cognitive-affective dynamics with Hidden Markov Models. In R. Catrambone & S. Ohlsson (Eds.), Proceedings of the 32nd Annual Cognitive Science Society (pp. 2721–2726). Austin: Cognitive Science Society.Google Scholar
  28. D’Mello, S., Lehman, B., & Graesser, A. (2011). A motivationally supportive affect-sensitive autotutor. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  29. D’Mello, S., Taylor, R., Davidson, K., & Graesser, A. (2008). Self versus teacher judgments of learner emotions during a tutoring session with AutoTutor. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the 9th international conference on Intelligent Tutoring Systems. Berlin: Springer.Google Scholar
  30. 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.Google Scholar
  31. Dalgleish, T., Dunn, B., & Mobbs, D. (2009). Affective neuroscience: Past, present, and future. Emotion Review, 1(4), 355–368.CrossRefGoogle Scholar
  32. Damasio, A. (2003). Looking for Spinoza: Joy, sorrow, and the feeling brain. Orlando: Harcourt.Google Scholar
  33. Daniels, L. M., Pekrun, R., Stupnisky, R. H., Haynes, T. L., Perry, R. P., & Newall, N. E. (2009). A longitudinal analysis of achievement goals: From affective antecedents to emotional effects and achievement outcomes. Journal of Educational Psychology, 101(4), 948–963.CrossRefGoogle Scholar
  34. Darwin, C. (1872). The expression of the emotions in man and animals. London: John Murray.CrossRefGoogle Scholar
  35. Dasarathy, B. (1997). Sensor fusion potential exploitation: Innovative architectures and illustrative approaches. Proceedings IEEE, 85, 24–38.CrossRefGoogle Scholar
  36. Davidson, R. J. (1998). Affective style and affective disorders: Perspectives from affective ­neuroscience. Cognition & Emotion, 12, 307–330.CrossRefGoogle Scholar
  37. Dewey, J. (1913). Interest and effort in education. Boston: Riverside.Google Scholar
  38. Dragon, T., Arroyo, I., Woolf, B. P., Burleson, W., el Kaliouby, R., & Eydgahi, H. (2008, Jun 23–Jul 27). Viewing student affect and learning through classroom observation and physical sensors. Paper presented at the 9th International Conference on Intelligent Tutoring Systems, Montreal.Google Scholar
  39. D’Mello, S., Lehman, B., & Person, N. (in press). Monitoring affect states during effortful problem solving activities. International Journal of Artificial Intelligence In Education.Google Scholar
  40. du Boulay, B. (2011). Towards a motivationally-intelligent pedagogy: How should an intelligent tutor respond to the unmotivated or the demotivated? In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  41. Dweck, C. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040–1048.CrossRefGoogle Scholar
  42. 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.Google Scholar
  43. Dweck, C. (2006). Mindset. New York: Random House.Google Scholar
  44. Ekman, P. (1984). Expression and the nature of emotion. In K. Scherer & P. Ekman (Eds.), Approaches to emotion (pp. 319–344). Hillsdale: Erlbaum.Google Scholar
  45. Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3–4), 169–200.CrossRefGoogle Scholar
  46. Festinger, L. (1957). A theory of cognitive dissonance. Stanford: Stanford University Press.Google Scholar
  47. Forgas, J. P. (1995). Mood and judgment – the affect infusion model (AIM). Psychological Bulletin, 117(1), 39–66.CrossRefGoogle Scholar
  48. Frijda, N. H., Mesquita, B., Sonnemans, J., & Van Goozen, S. (1991). The duration of affective phenomena, or emotions, sentiments, and passions. In K. Strongman (Ed.), International review of emotion and motivation (pp. 187–225). New York: Wiley.Google Scholar
  49. Garrett, A. S., & Maddock, R. J. (2001). Time course of the subjective emotional response to aversive pictures: Relevance to fMRI studies. Psychiatry Research, 108(1), 39–48.CrossRefGoogle Scholar
  50. Goodyear, P. (2011). Affect, technology and convivial learning environments. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  51. Gotlib, I., & Abramson, L. (1999). Attributional theories of emotion. In T. Dalgleish & M. Power (Eds.), Handbook of cognition and emotion. Wiley: Sussex.Google Scholar
  52. 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.CrossRefGoogle Scholar
  53. Graesser, A., McDaniel, B., Chipman, P., Witherspoon, A., D’Mello, S., & Gholson, B. (2006). Detection of emotions during learning with AutoTutor. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 285–290). Austin: Cognitive Science Society.Google Scholar
  54. Graesser, A., & Olde, B. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology, 95(3), 524–536.CrossRefGoogle Scholar
  55. Gross, J. (1998). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2, 271–299.CrossRefGoogle Scholar
  56. Gross, J. (2008). Emotion regulation. In M. Lewis, J. Haviland-Jones, & L. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 497–512). New York: Guilford.Google Scholar
  57. Guthrie, J. T., Wigfield, A., Humenick, N. M., Perencevich, K. C., Taboada, A., & Barbosa, P. (2006). Influences of stimulating tasks on reading motivation and comprehension. Journal of Educational Research, 99(4), 232–245.CrossRefGoogle Scholar
  58. Harter, S. (1992). The relationship between perceived competence, affect, and motivational orientation within the classroom: Process and patterns of change. In A. Boggiano & T. Pittman (Eds.), Achievement and motivation: A social-developmental perspective (pp. 77–114). New York: Cambridge University Press.Google Scholar
  59. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.CrossRefGoogle Scholar
  60. Hemenover, S. H. (2003). Individual differences in rate of affect change: Studies in affective ­chronometry. Journal of Personality and Social Psychology, 85, 121–131.CrossRefGoogle Scholar
  61. Hidi, S. (2006). Interest: A unique motivational variable. Educational Research Review, 1, 69–82.CrossRefGoogle Scholar
  62. Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.CrossRefGoogle Scholar
  63. Isen, A. (2008). Some ways in which positive affect influences decision making and problem ­solving. In M. Lewis, J. Haviland-Jones, & L. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 548–573). New York: Guilford.Google Scholar
  64. Izard, C. E. (2007). Basic emotions, natural kinds, emotion schemas, and a new paradigm. Perspectives on Psychological Science, 2(3), 260–280.CrossRefGoogle Scholar
  65. Joormann, J. (2010). Cognitive inhibition and emotion regulation in depression. Current Directions in Psychological Science, 19(3), 161–166.CrossRefGoogle Scholar
  66. Lazarus, R. (1991). Emotion and adaptation. New York: Oxford University Press.Google Scholar
  67. Lazarus, R. (2000). The cognition-emotion debate: A bit of history. In M. Lewis & J. Haviland-Jones (Eds.), Handbook of emotions (2nd ed., pp. 1–20). New York: Guilford Press.Google Scholar
  68. 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.Google Scholar
  69. 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.CrossRefGoogle Scholar
  70. Lester, J. C., McQuiggan, S. W., & Sabourin, J. L. (2011). Affect recognition and expression in narrative-centered learning environments. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  71. Lewis, M. D. (2005). Bridging emotion theory and neurobiology through dynamic systems modeling. Behavioral and Brain Sciences, 28(2), 169–245.Google Scholar
  72. Linnenbrink, E. (2007). The role of affect in student learning: A mulit-dimensional approach to considering the interaction of affect, motivation and engagement. In P. Schutz & R. Pekrun (Eds.), Emotions in education (pp. 107–124). San Diego: Academic.Google Scholar
  73. Mandler, G. (1976). Mind and emotion. New York: Wiley.Google Scholar
  74. Mandler, G. (1984). Mind and body: Psychology of emotion and stress. New York: W.W. Norton & Company.Google Scholar
  75. Mandler, G. (1999). Emotion. In B. M. Bly & D. E. Rumelhart (Eds.), Cognitive science. Handbook of perception and cognition (2nd ed., pp. 367–382). San Diego: Academic.Google Scholar
  76. Meyer, D., & Turner, J. (2006). Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review, 18(4), 377–390.CrossRefGoogle Scholar
  77. Norman, D. A. (2005). Emotional design: Why we love (or hate) everyday things. New York: Basic Books.Google Scholar
  78. Ortony, A., Clore, G., & Collins, A. (1988). The cognitive structure of emotions. New York: Cambridge University Press.Google Scholar
  79. Parkinson, B. (1995). Ideas and realities of emotion. London: Routledge.Google Scholar
  80. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341.CrossRefGoogle Scholar
  81. Pekrun, R. (2010). Academic emotions. In T. Urdan (Ed.), APA educational psychology handbook (Vol. 2). Washington: American Psychological Association.Google Scholar
  82. Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  83. Pekrun, R., Elliot, A., & Maier, M. (2006). Achievement goals and discrete achievement emotions: A theoretical model and prospective test. Journal of Educational Psychology, 98(3), 583–597.CrossRefGoogle Scholar
  84. Pekrun, R., Goetz, T., Daniels, L., Stupnisky, R. H., & Raymond, P. (2010). Boredom in achievement settings: Exploring control–value antecedents and performance outcomes of a neglected emotion. Journal of Educational Psychology, 102(3), 531–549.CrossRefGoogle Scholar
  85. Peterson, G. (2006). Cultural theory and emotions. In J. Stets & J. Turner (Eds.), Handbook of the sociology of emotions (pp. 114–134). New York: Springer.CrossRefGoogle Scholar
  86. Piaget, J. (1952). The origins of intelligence. New York: International University Press.CrossRefGoogle Scholar
  87. Picard, R. (1997). Affective computing. Cambridge: MIT Press.Google Scholar
  88. Picard, R. (2010). Affective computing: From laughter to IEEE. IEEE Transactions on Affective Computing, 1(1), 11–17.CrossRefGoogle Scholar
  89. Picard, R., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. Ieee Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175–1191.CrossRefGoogle Scholar
  90. Rebolledo-Mendez, G., Luckin, R., & du Boulay, B. (2011). Designing adaptive motivational scaffolding for a tutoring system. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.Google Scholar
  91. Rosenberg, E. (1998). Levels of analysis and the organization of affect. Review of General Psychology, 2(3), 247–270.CrossRefGoogle Scholar
  92. Rosenberg, E., & Ekman, P. (1994). Coherence between expressive and experiential systems in emotion. Cognition & Emotion, 8(3), 201–229.CrossRefGoogle Scholar
  93. Russell, J. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110, 145–172.CrossRefGoogle Scholar
  94. Russell, J. A., Bachorowski, J. A., & Fernandez-Dols, J. M. (2003). Facial and vocal expressions of emotion. Annual Review of Psychology, 54, 329–349.CrossRefGoogle Scholar
  95. Salovey, P. (2003). Introduction: Emotion and social processes. In R. Davidson, K. Scherer, & H. Goldsmith (Eds.), Handbook of affective sciences. New York: Oxford University Press.Google Scholar
  96. Scherer, K. R. (2009a). The dynamic architecture of emotion: Evidence for the component process model. Cognition & Emotion, 23(7), 1307–1351.CrossRefGoogle Scholar
  97. Scherer, K. R. (2009b). Emotions are emergent processes: They require a dynamic computational architecture. Philosophical Transactions of the Royal Society B-Biological Sciences, 364(1535), 3459–3474.CrossRefGoogle Scholar
  98. Scherer, K., & Ellgring, H. (2007). Multimodal expression of emotion: Affect programs or componential appraisal patterns? Emotion, 7(1), 158–171.CrossRefGoogle Scholar
  99. Scherer, K., Schorr, A., & Johnstone, T. (Eds.). (2001). Appraisal processes in emotion: Theory, methods, research. London: London University Press.Google Scholar
  100. Schultz, P., & Pekrun, R. (Eds.). (2007). Emotion in education. San Diego: Academic.Google Scholar
  101. Schutz, P., & Pekrun, R. (Eds.). (2007). Emotion in education. San Diego: Academic.Google Scholar
  102. Schwartz, D., & Bransford, D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–522.CrossRefGoogle Scholar
  103. Schwarz, N. (1990). Feelings as information: Informational and motivational functions of affective states. In E. Higgins & R. Sorrentino (Eds.), Handbook of motivation and cognition (pp. 527–561). New York: Guilford Press.Google Scholar
  104. Schwarz, N. (in press). Feelings-as-information theory. In P. Van Lange, A. Kruglanski & T. Higgins (Eds.), Handbook of theories of social psychology. Sage.Google Scholar
  105. Smith, C., & Ellsworth, P. (1985). Patterns of cognitive appraisal in emotion. Journal of Personality and Social Psychology, 48(4), 813–838.CrossRefGoogle Scholar
  106. Stein, N. L., & Albro, E. R. (2001). The origins and nature of arguments: Studies in conflict understanding, emotion, and negotiation. Discourse Processes, 32(2), 113–133.CrossRefGoogle Scholar
  107. 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.Google Scholar
  108. Stein, N., & Levine, L. (1991). Making sense out of emotion. In A. O. W. Kessen & F. Kraik (Eds.), Memories thoughts and emotions: Essays in honor of George Mandler (pp. 295–322). Hillsdale: Erlbaum.Google Scholar
  109. Stets, J., & Turner, J. (2008). The sociology of emotions. In M. Lewis, J. Haviland-Jones, & L. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 32–46). New York: Guilford.Google Scholar
  110. Stipek, D. (1988). Motivation to learn: From theory to practice. Boston: Allyn and Bacon.Google Scholar
  111. Strain, A. C., & D’Mello, S.K. (in press). Emotion regulation during learning. In S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education (pp. 566–538). New York / Heidelberg: Springer.Google Scholar
  112. Tobias, S. (1994). Interest, prior knowledge, and learning. Review of Educational Research, 64, 37–54.Google Scholar
  113. Tomkins, S. S. (1962). Affect imagery consciousness: Volume I, the positive affects. London: Tavistock.Google Scholar
  114. VanLehn, K., Siler, S., Murray, C., Yamauchi, T., & Baggett, W. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 21(3), 209–249.CrossRefGoogle Scholar
  115. Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. Cambridge: Harvard University Press.Google Scholar
  116. Wassmann, C. (2010). Reflections on the body loop: Carl Georg Lange’s theory of emotion. Cognition & Emotion, 24(6), 974–990.CrossRefGoogle Scholar
  117. Watson, D., & Clark, L. A. (1994). Emotions, moods, traits, and temperaments: Conceptual distinctions and empirical findings. In P. Ekman & J. Davidson (Eds.), The nature of emotion: Fundamental questions (pp. 89–93). New York: Oxford University Press.Google Scholar
  118. Weiner, B. (1986). An attributional theory of motivation and emotion. New York: Springer.Google Scholar
  119. Woolf, B., Arroyo, I., Muldner, K., Burleson, W., Cooper, D., Dolan, R., et al. (2010). The effect of motivational learning companions on low achieving students and students with disabilities. In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems (pp. 327–337). Berlin: Springer.Google Scholar
  120. Yoshitomi, Y., Sung-Ill, K., Kawano, T., & Kilazoe, T. (2000). Effect of sensor fusion for recognition of emotional states using voice, face image and thermal image of face. In P. o. t. I. I. W. o. R. a. H. I. Communication (Ed.), (pp. 178–183). Osaka: IEEE.Google Scholar
  121. Zeidner, M. (2007). Test anxiety in educational contexts: Concepts, findings, and future directions. In P. Schutz & R. Pekrun (Eds.), Emotions in education (pp. 165–184). San Diego: Academic.Google Scholar
  122. Zeng, Z., Pantic, M., Roisman, G., & Huang, T. (2009). A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 39–58.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Psychology Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

Personalised recommendations