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Instructional Science

, Volume 47, Issue 2, pp 151–180 | Cite as

Emotion regulation tendencies, achievement emotions, and physiological arousal in a medical diagnostic reasoning simulation

  • Jason M. HarleyEmail author
  • Amanda Jarrell
  • Susanne P. Lajoie
Original Research

Abstract

Despite the importance of emotion regulation in education there is a paucity of research examining it in authentic educational contexts. Moreover, emotion measurement continues to be dominated by self-report measures. We address these gaps in the literature by measuring emotion regulation and activation in 37 medical students’ who were solving medical cases using BioWorld, a computer based learning environment. Specifically, we examined students’ habitual use of emotion regulation strategies as well as electrodermal activation (emotional arousal) from skin conductance level (SCL) or skin conductance response (SCR), as well as appraisals of control and value and self-reported emotional responses during a diagnostic reasoning task in Bioworld. Our results revealed that medical students reported significantly higher habitual levels of reappraisal than suppression ER strategies. Higher habitual levels of reappraisal significantly and positively predicted learners’ self-reported pride. On the other hand, higher habitual levels of suppression significantly and positively predicted learners’ self-reported anxiety, shame, and hopelessness. Results also revealed that medical students experienced relatively low SCLs and few SCRs while interacting with Bioworld. Habitual suppression strategies significantly and positively predicted medical students’ SCLs, while SCRs significantly and positively predicted their diagnostic efficiency. Findings also revealed a significant, positive predictive relationship between SCL and shame and anxiety and the inverse relationship between SCL and task value. Implications and future directions are discussed.

Keywords

Emotion Affect Emotion regulation Physiological Arousal Activation Skin conductance 

Notes

Acknowledgements

This research was supported by funding from the Social Sciences and Humanities Research Council of Canada (grant number: 895-2011-1006).

Compliance with Ethical Standards

Conflict of interest

The authors do not have any conflicts of interest to report.

References

  1. Arroyo, I., Burleson, W., Tai, M., Muldner, K., & Woolf, B. P. (2013). Gender differences in the use and benefit of advanced learning technologies for mathematics. Journal of Educational Psychology, 105, 957–969.Google Scholar
  2. Artino, A. R., Jr., Holmboe, E. S., & Durning, S. J. (2012). Can achievement emotions be used to better understand motivation, learning, and performance in medical education? Medical Teacher, 34(3), 240–244.Google Scholar
  3. Artino, A. R., Jr., & Pekrun, R. (2014). Using control-value theory to understand achievement emotions in medical education. Academic Medicine, 89(12), 1696.Google Scholar
  4. Austin, P. C., & Steyerberg, E. W. (2015). The number of subjects per variable required in linear regression analyses. Journal of Clinical Epidemiology, 68(6), 627–636.Google Scholar
  5. Baker, R., Rodrigo, M., & Xolocotzin, U. (2007). The dynamics of affective transitions in simulation problem solving environments. In A. R. Paiva, R. Prada, & R. Picard (Eds.), Affective computing and intelligent interaction (Vol. 4738, pp. 666–677). Berlin: Springer.Google Scholar
  6. Boucsein, W. (2012). Electrodermal activity. New York: Springer.Google Scholar
  7. Braithwaite, J. J., Watson, D. G., Jones, R., & Rowe, M. A. (2013). Guide for analyzing electrodermal activity (EDA) & skin conductance responses (SCRs) for psychological experiments. Psychophysiology, 49, 1017–1034.Google Scholar
  8. Burić, I., Sorić, I., & Penezić, Z. (2016). Emotion regulation in academic domain: Development and validation of the academic emotion regulation questionnaire (AERQ). Personality and Individual Differences, 96, 138–147.  https://doi.org/10.1016/j.paid.2016.02.074.CrossRefGoogle Scholar
  9. Butler, E. A., Wilhelm, F. H., & Gross, J. J. (2006). Respiratory sinus arrhythmia, emotion, and emotion regulation during social interaction. Psychophysiology, 43(6), 612–622.Google Scholar
  10. Cacioppo, J. T., Tassinary, L. G., & Berntson, G. (Eds.). (2007). Handbook of psychophysiology. Cambridge: Cambridge University Press.Google Scholar
  11. Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 18–37.Google Scholar
  12. Chauncey-Strain, A., & D’Mello, S. K. (2015). Affect regulation during learning: The enhancing effect of cognitive reappraisal. Applied Cognitive Psychology, 29, 1–19.Google Scholar
  13. Cohen, R. A. (2011). Yerkes-Dodson Law. In J. S. Kreutzer, J. DeLuca, & B. Caplan (Eds.), Encyclopedia of clinical neuropsychology (pp. 2737–2738). New York: Springer.Google Scholar
  14. Curran-Everett, D. (2000). Multiple comparisons: Philosophies and illustrations. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 279(1), R1–R8.Google Scholar
  15. Dan-Glauser, E. S., & Gross, J. J. (2013). Emotion regulation and emotion coherence: Evidence for strategy-specific effects. Emotion, 13, 832.Google Scholar
  16. Daniels, L. M., Haynes, T. L., Stupnisky, R. H., Perry, R. P., Newall, N. E., & Pekrun, R. (2008). Individual differences in achievement goals: A longitudinal study of cognitive, emotional, and achievement outcomes. Contemporary Educational Psychology, 33(4), 584–608.Google Scholar
  17. Dawson, M.E., et al (2001) The Electrodermal System. In J. T. Cacioppo, L. G. Tassinary, and G.B. Bernston, (Eds) Handbook of Psychophysiology (2nd Ed), 200–223. Cambridge Press, CambridgeGoogle Scholar
  18. Decuir-Gunby, J. T., Aultman, L. P., & Schutz, P. A. (2009). Investigating transactions among motives, emotional regulation related to testing, and test emotions. The Journal of Experimental Education, 77(4), 409–438.  https://doi.org/10.3200/jexe.77.4.409-438.CrossRefGoogle Scholar
  19. D’Mello, S. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105(4), 1082.Google Scholar
  20. D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., et al. (2010). A time for emoting: When affect-sensitivity is and isn’t effective at promoting deep learning. In V. Aleven, J. Kay, & J. Mostow (Eds.), Lecture notes in computer science (Vol. 6094, pp. 245–254)., Intelligent tutoring systems Berlin: Springer.Google Scholar
  21. D’Mello, S. K., & Graesser, A. C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), Handbook of affective computing (pp. 419–434). Oxford: Oxford University Press.Google Scholar
  22. D’Mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys (CSUR), 47(3), 43.Google Scholar
  23. Duffy, M. C., Azevedo, R., Sun, N. Z., Griscom, S. E., Stead, V., Crelinsten, L., et al. (2015). Team regulation in a simulated medical emergency: An in-depth analysis of cognitive, metacognitive, and affective processes. Instructional Science, 43(3), 401–426.Google Scholar
  24. Duffy, M. C., Lajoie, S. P., Pekrun, R., & Lachapelle, K. (2018). Emotions in medical education: Examining the validity of the Medical Emotion Scale (MES) across authentic medical learning environments. Learning and Instruction.  https://doi.org/10.1016/j.learninstruc.2018.07.001
  25. Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3), 169–200.Google Scholar
  26. Evers, C., Hopp, H., Gross, J. J., Fischer, A., Manstead, A., & Mauss, I. (2014). Emotion response coherence: A dual-process perspective. Biological Psychology, 98, 43–49.Google Scholar
  27. Goetz, T., Bieg, M., Lüdtke, O., Pekrun, R., & Hall, N. C. (2013). Do girls really experience more anxiety in mathematics? Psychological science, 24(10), 2079–2087.  https://doi.org/10.1177/0956797613486989.CrossRefGoogle Scholar
  28. Goetz, T., Frenzel, A. C., Hall, N. C., Nett, U. E., Pekrun, R., & Lipnevich, A. A. (2014). Types of boredom: An experience sampling approach. Motivation and Emotion, 38(3), 401–419.Google Scholar
  29. Goetz, T., & Hall, N. C. (2013). Emotion and achievement in the classroom. In J. Hattie & E. M. Anderman (Eds.), International guide to student achievement (pp. 192–195). New York: Routledge.Google Scholar
  30. Green, S. B. (1991). How many subjects does it take to do a regression analysis. Multivariate Behavioral Research, 26(3), 499–510.Google Scholar
  31. Gross, J. J. (1998a). Antecedent-and response-focused emotion regulation: divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74(1), 224.Google Scholar
  32. Gross, J. J. (1998b). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2(3), 271.Google Scholar
  33. Gross, J. J. (2002). Emotion regulation: Affective, cognitive, and social consequences. Psychophysiology, 39, 281–291.Google Scholar
  34. Gross, J. J. (2015). The extended process model of emotion regulation: Elaborations, applications, and future directions. Psychological Inquiry, 26(1), 130–137.Google Scholar
  35. Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85, 348–362.Google Scholar
  36. Gross, J. J., & Levenson, R. W. (1993). Emotional suppression: Physiology, self-report, and expressive behavior. Journal of Personality and Social Psychology, 64, 970–986.Google Scholar
  37. Harley, J. M. (2015). Measuring emotions: A survey of cutting-edge methodologies used in computer-based learning environment research. In S. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 89–114). London: Academic Press.Google Scholar
  38. Harley, J. M., Bouchet, F., & Azevedo, R. (2013). Aligning and comparing data on learners’ emotions experienced with MetaTutor. In C. H. Lane, K. Yacef, J. Mostow, P. Pavik (Eds.), Lecture Notes in Artificial Intelligence: Vol. 7926. Artificial Intelligence in Education (pp. 61–70). Berlin: Springer.Google Scholar
  39. Harley, J. M., Bouchet, F., Hussain, S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615–625.  https://doi.org/10.1016/j.chb.2015.02.013.CrossRefGoogle Scholar
  40. Harley, J. M., Carter, C. K., Papaionnou, N., Bouchet, F., Azevedo, R., Landis, R. L., et al. (2016a). Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: Towards emotionally-adaptive agent-based learning environments. User Modeling and User-Adapted Interaction, 26, 177–219.  https://doi.org/10.1007/s11257-016-9169-7.CrossRefGoogle Scholar
  41. Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N. C. (2017). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International Journal of Artificial Intelligence in Education, 27(2), 268–297.  https://doi.org/10.1007/s40593-016-0126-8.CrossRefGoogle Scholar
  42. Harley, J.M., Lajoie, S.P., Tressel, T., & Jarrell, A. (2018). Fostering positive emotions and history learning with location-based augmented reality and tour-guide prompts. Learning & Instruction.  https://doi.org/10.1016/j.learninstruc.2018.09.001
  43. Harley, J. M., Poitras, E. G., Jarrell, A., Duffy, M. C., & Lajoie, S. P. (2016b). Comparing virtual and location-based augmented reality mobile learning: Emotions and learning outcomes. Educational Technology Research and Development, 64(3), 359–388.  https://doi.org/10.1007/s11423-015-9420-7.CrossRefGoogle Scholar
  44. Harrell, F. E., Jr. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis. New York: Springer.Google Scholar
  45. Hussain, S. M., D’Mello, S. K., & Calvo, R. A. (2014). Research and development tools in affective computing. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The oxford handbook of affective computing (pp. 349–359). Oxford: Oxford University Press.Google Scholar
  46. Jamieson, J. P., Mendes, W. B., Blackstock, E., & Schmader, T. (2010). Turning the knots in your stomach into bows: Reappraising arousal improves performance on the GRE. Journal of Experimental Social Psychology, 46(1), 208–212.Google Scholar
  47. Jarrell, A., Harley, J. M., & Lajoie, S. P. (2016). The link between achievement emotions, appraisals and task performance: Pedagogical considerations for emotions in CBLEs. Journal of Computers in Education, 3(3), 289–307.  https://doi.org/10.1007/s40692-016-0064-3.CrossRefGoogle Scholar
  48. Jarrell, A., Harley, J. M., Lajoie, S. P., & Naismith, L. (2017). Success, failure and emotions: Examining the relationship between performance feedback and emotions in diagnostic reasoning. Educational Technology Research and Development, 65(5), 1263–1284.  https://doi.org/10.1007/s11423-017-9521-6.CrossRefGoogle Scholar
  49. Kapoor, A., Burleson, W., & Picard, R. W. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65, 724–736.Google Scholar
  50. Kreibig, S. D., Samson, A. C., & Gross, J. J. (2015). The psychophysiology of mixed states: Internal and external replicability analysis of a direct replication study. Psychophysiology, 52, 873–886.Google Scholar
  51. Lajoie, S. (2009). Developing professional expertise with a cognitive apprenticeship model: Examples from avionics and medicine. In K. A. Ericsson (Ed.), Development of professional expertise: Toward measurement of expert performance and design of optimal learning environments (pp. 61–83). Cambridge: Cambridge University Press.Google Scholar
  52. Lajoie, S. P., Lee, L., Poitras, E., Bassiri, M., Kazemitabar, M., Cruz-Panesso, I., et al. (2015). The role of regulation in medical student learning in small groups: Regulating oneself and others’ learning and emotions. Computers in Human Behavior, 52, 601–616.Google Scholar
  53. Leroy, V., Gregoire, J., Magen, E., Gross, J. J., & Mikolajczak, M. (2012). Resisting the sirens of temptation while studying: Using reappraisal to increase enthusiasm and performance. Learning and Individual Differences, 22, 263–268.Google Scholar
  54. Li, Z., Snieder, H., Su, S., Ding, X., Thayer, J. F., Treiber, F. A., et al. (2009). A longitudinal study in youth of heart rate variability at rest and in response to stress. International Journal of Psychophysiology, 73(3), 212–217.Google Scholar
  55. Matsunaga, M. (2007). Familywise error in multiple comparisons: Disentangling a knot through a critique of O’Keefe’s arguments against Alpha Adjustment. Communication Methods and Measures, 1(4), 243–265.Google Scholar
  56. Mauss, I. B., Cook, C. L., Cheng, J. Y., & Gross, J. J. (2007). Individual differences in cognitive reappraisal: Experiential and physiological responses to an anger provocation. International Journal of Psychophysiology, 66(2), 116–124.Google Scholar
  57. Mauss, I. B., Levenson, R. W., McCarter, L., Wilhelm, F. H., & Gross, J. J. (2005). The tie that binds? Coherence among emotion experience, behavior, and physiology. Emotion, 5(2), 175–190.Google Scholar
  58. Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and Emotion, 23(2), 209–237.Google Scholar
  59. McQuiggan, S. W., Robison, J. L., & Lester, J. C. (2010). Affective transitions in narrative-centered learning environments. Educational Technology & Society, 13(1), 40–53.Google Scholar
  60. Meinhardt, J., & Pekrun, R. (2003). Attentional resource allocation to emotional events: An ERP study. Cognition and Emotion, 17, 477–500.Google Scholar
  61. Nagai, Y., Critchley, H. D., Featherstone, E., Trimble, M. R., & Dolan, R. J. (2004). Activity in ventromedial prefrontal cortex covaries with sympathetic skin conductance level: A physiological account of a ‘‘default mode’’ of brain function. NeuroImage, 22, 243–251.Google Scholar
  62. Naismith, L. M., & Lajoie, S. P. (2018). Motivation and emotion predict medical students’ attention to computer-based feedback. Advances in Health Sciences Education, 23, 465–485.  https://doi.org/10.1007/s10459-017-9806-x.CrossRefGoogle Scholar
  63. Nett, U. E., Goetz, T., & Hall, N. C. (2011). Coping with boredom in school: An experience sampling perspective. Contemporary Educational Psychology, 36(1), 49–59.  https://doi.org/10.1016/j.cedpsych.2010.10.003.CrossRefGoogle Scholar
  64. O’Keefe, D. J. (2003). Colloquy: Should familywise alpha be adjusted? Human Communication Research, 29(3), 431–447.Google Scholar
  65. Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41, 359–376.Google Scholar
  66. Pekrun, R. (2006). The control-value theory of achievement emotions. Educational Psychology Review, 18(4), 315–341.Google Scholar
  67. Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. A. Calvo & S. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 23–39). New York: Springer.Google Scholar
  68. Pekrun, R., Elliot, A. J., & Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101(1), 115.Google Scholar
  69. Pekrun, R., Goetz, T., Frenzel-Anne, C., Petra, B., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36, 34–48.Google Scholar
  70. Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of quantitative and qualitative research. Educational Psychologist, 37, 91–106.Google Scholar
  71. Pekrun, R., Hall, N. C., Goetz, T., & Perry, R. (2014). Boredom and academic achievement: Testing a model of reciprocal causation. Journal of Educational Psychology, 106, 696–710.Google Scholar
  72. Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: A longitudinal model of reciprocal effects. Child Development, 88(5), 1653–1670.Google Scholar
  73. Pekrun, R., & Linnenbrink-Garcia, L. (2014). International handbook of emotions in education. New York: Routledge.Google Scholar
  74. Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120–141). New York: Routledge.Google Scholar
  75. Picard, R. W., Fedor, S., & Ayzenberg, Y. (2016). Multiple arousal theory and daily-life electrodermal activity asymmetry. Emotion Review, 8, 62–75.Google Scholar
  76. Porayska-Pomsta, K., Mavrikis, M., Dmello, S., Conati, C., & Baker, R. S. (2013). Knowledge elicitation methods for affect modeling in education. International Journal of Artificial Intelligence in Education, 22, 107–140.Google Scholar
  77. Q-Sensor 2.0 Apparatus and software. (2013). Waltham, MA: Affectiva.Google Scholar
  78. Robison, J., McGuiggan, S. W., & Lester, J. (2009). Evaluating the consequences of affective feedback in intelligent tutoring systems. In J. Cohn, A. Nijholt, & M. Pantic (Eds.). Proceedings of the international conference on affective computing & intelligent interaction (pp. 37–42). Amsterdam: IEEE Press.Google Scholar
  79. Rubin, M. (2017). Do p values lose their meaning in exploratory analyses? It depends how you define the familywise error rate. Review of General Psychology, 21(3), 269.Google Scholar
  80. Russel, J. A., Weiss, A., & Mendelsohn, G. A. (1989). Affect grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57(3), 493–502.Google Scholar
  81. Sabourin, J. L., & Lester, J. C. (2014). Affect and engagement in game-based learning environments. IEEE Transactions on Affective Computing, 5, 45–55.Google Scholar
  82. Scherer, K. R. (1984). On the nature and function of emotion: A component process approach. In K. R. Scherer & P. Ekman (Eds.), Approaches to emotion (pp. 293–317). Hillsdale, NJ: Erlbaum.Google Scholar
  83. Schmidt, F. L. (1971). The relative efficiency of regression and simple unit predictor weights in applied differential psychology. Educational and Psychological Measurement, 31(3), 699–714.Google Scholar
  84. Scrimin, S., Altoè, G., Moscardino, U., Pastore, M., & Mason, L. (2016). Individual differences in emotional reactivity and academic achievement: A psychophysiological study. Mind, Brain, and Education, 10(1), 34–46.Google Scholar
  85. Shute, V. J., D’Mello, S., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., et al. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86, 224–235.Google Scholar
  86. Spangler, G., Pekrun, R., Kramer, K., & Hofmann, H. (2002). Students’ emotions, physiological reactions, and coping in academic exams. Anxiety Stress and Coping, 15(4), 413–432.Google Scholar
  87. Steinfatt, T. M. (1979). The alpha percentage and experimentwise error rates in communication research. Human Communication Research, 5(4), 366–374.Google Scholar
  88. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson Education/Allyn and Bacon.Google Scholar
  89. Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2017). Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island. Computers in Human Behavior, 76, 641–655.Google Scholar
  90. Turner, J. E., & Schallert, D. L. (2001). Expectency-value relationships of shame reactions and shame resilience. Journal of Educational Psychology, 93, 320–329.Google Scholar
  91. Webb, T. L., Miles, E., & Sheeran, P. (2012). Dealing with feeling: A meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psychological Bulletin, 138(4), 775–808.Google Scholar
  92. Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affectaware tutors: Recognizing and responding to student affect. International Journal of Learning Technology, 4, 129–164.Google Scholar
  93. Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology, 18(5), 459–482.Google Scholar

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Authors and Affiliations

  1. 1.Department of Educational PsychologyUniversity of AlbertaEdmontonCanada
  2. 2.Department of Educational and Counselling PsychologyMcGill UniversityMontréalCanada

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