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.
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Participants were not asked if they interacted with similar environments to this one, but given the novelty and specificity of BioWorld it is very unlikely.
Students from first year onward in medical school stood to benefit from interacting with BioWorld which provides diagnostic reasoning training, therefore we did not exclude participants based on year of medical studies.
Instructors did not have a role in the experiment. Research assistants that were part of the lab that conducted this study managed the experimental protocol.
No comparisons with participants who wore an SCR bracelet were made because these participants were never directly compared (see Table 1; all usable SCR data came from participants who interacted with Case 1 or 2: n = 14).
“Type I error is and should be localized by H0 because Type I error refers to the error of falsely rejecting a given null hypothesis when it should not be rejected (e.g., Curran-Everett 2000). In other words, identification of the scope of a given H0 leads to the proper localization of Type I error, which, in turn, dictates how the respective alpha level should be adjusted.” (Matsunaga 2007, p. 251).
While p = .050 might be considered marginally significant rather than significant, it is squarely on the fence of p < .05. Moreover, this result was significant prior to outlier cleaning. Given that no single approach to outlier cleaning can deal perfectly with outliers and their influence on the distribution, the prior significance of this finding to a single outlier being removed, and the difference in reporting versus not reporting being a value of .001, we opted to refer to this variable as significant rather than marginally significant, as we do not typically report marginally significant results.
The academic achievement emotion questionnaire data was not collected for participants who wore the Biopac bracelet. See limitations for more details.
According to the CVT, pride can be elicited when students appraise an outcome with positive value (e.g., success) and believes that they are responsible for this outcome (Pekrun 2006). Pride during a performance task, such as a test, can be attributed to their level of knowledge and performance (Pekrun 2006; Pekrun et al. 2002). Likewise, during the diagnostic reasoning task, students can feel pride in relation to their knowledge of different diseases, symptoms, and presentations, and their perceived performance as they move through solving the case. For example, a student could feel proud that they were able to correctly select the laboratory test that enabled them to identify an abnormal test result indicative of a particular disease. Given that students can feel pride during diagnostic reasoning, it is possible that individual differences in emotion regulation could be associated with this emotion. Previous research in the context of test-taking and learning have also tested this possibility and found that wishful thinking and self-blame are negatively related to feelings of test pride (Decuir-Gunby et al. 2009) and habitual reappraisal is positively related to feelings of learning-related pride (Buric et al. 2016). Therefore, it is possible and reasonable to suggest that habitual reappraisal could be related to pride in the context of diagnostic reasoning.
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.
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.
Artino, A. R., Jr., & Pekrun, R. (2014). Using control-value theory to understand achievement emotions in medical education. Academic Medicine, 89(12), 1696.
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.
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.
Boucsein, W. (2012). Electrodermal activity. New York: Springer.
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.
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.
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.
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. (Eds.). (2007). Handbook of psychophysiology. Cambridge: Cambridge University Press.
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.
Chauncey-Strain, A., & D’Mello, S. K. (2015). Affect regulation during learning: The enhancing effect of cognitive reappraisal. Applied Cognitive Psychology, 29, 1–19.
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.
Curran-Everett, D. (2000). Multiple comparisons: Philosophies and illustrations. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, 279(1), R1–R8.
Dan-Glauser, E. S., & Gross, J. J. (2013). Emotion regulation and emotion coherence: Evidence for strategy-specific effects. Emotion, 13, 832.
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.
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, Cambridge
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.
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.
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.
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.
D’Mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys (CSUR), 47(3), 43.
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.
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
Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3), 169–200.
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.
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.
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.
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.
Green, S. B. (1991). How many subjects does it take to do a regression analysis. Multivariate Behavioral Research, 26(3), 499–510.
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.
Gross, J. J. (1998b). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2(3), 271.
Gross, J. J. (2002). Emotion regulation: Affective, cognitive, and social consequences. Psychophysiology, 39, 281–291.
Gross, J. J. (2015). The extended process model of emotion regulation: Elaborations, applications, and future directions. Psychological Inquiry, 26(1), 130–137.
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.
Gross, J. J., & Levenson, R. W. (1993). Emotional suppression: Physiology, self-report, and expressive behavior. Journal of Personality and Social Psychology, 64, 970–986.
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.
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.
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.
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.
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.
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
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.
Harrell, F. E., Jr. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis. New York: Springer.
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.
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.
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.
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.
Kapoor, A., Burleson, W., & Picard, R. W. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65, 724–736.
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.
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.
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.
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.
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.
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.
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.
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.
Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and Emotion, 23(2), 209–237.
McQuiggan, S. W., Robison, J. L., & Lester, J. C. (2010). Affective transitions in narrative-centered learning environments. Educational Technology & Society, 13(1), 40–53.
Meinhardt, J., & Pekrun, R. (2003). Attentional resource allocation to emotional events: An ERP study. Cognition and Emotion, 17, 477–500.
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.
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.
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.
O’Keefe, D. J. (2003). Colloquy: Should familywise alpha be adjusted? Human Communication Research, 29(3), 431–447.
Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41, 359–376.
Pekrun, R. (2006). The control-value theory of achievement emotions. Educational Psychology Review, 18(4), 315–341.
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.
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.
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.
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.
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.
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.
Pekrun, R., & Linnenbrink-Garcia, L. (2014). International handbook of emotions in education. New York: Routledge.
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.
Picard, R. W., Fedor, S., & Ayzenberg, Y. (2016). Multiple arousal theory and daily-life electrodermal activity asymmetry. Emotion Review, 8, 62–75.
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.
Q-Sensor 2.0 Apparatus and software. (2013). Waltham, MA: Affectiva.
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.
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.
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.
Sabourin, J. L., & Lester, J. C. (2014). Affect and engagement in game-based learning environments. IEEE Transactions on Affective Computing, 5, 45–55.
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.
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.
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.
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.
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.
Steinfatt, T. M. (1979). The alpha percentage and experimentwise error rates in communication research. Human Communication Research, 5(4), 366–374.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson Education/Allyn and Bacon.
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.
Turner, J. E., & Schallert, D. L. (2001). Expectency-value relationships of shame reactions and shame resilience. Journal of Educational Psychology, 93, 320–329.
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.
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.
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.
This research was supported by funding from the Social Sciences and Humanities Research Council of Canada (grant number: 895-2011-1006).
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Harley, J.M., Jarrell, A. & Lajoie, S.P. Emotion regulation tendencies, achievement emotions, and physiological arousal in a medical diagnostic reasoning simulation. Instr Sci 47, 151–180 (2019). https://doi.org/10.1007/s11251-018-09480-z
- Emotion regulation
- Skin conductance