Abstract
Students experience a variety of emotions following achievement outcomes which stand to influence how they learn and perform in academic settings. However, little is known about the link between student outcome emotions and dimensions of performance feedback in computer-based learning environments (CBLEs). Understanding the dynamics of this relationship is particularly important for high-stakes, competency-based domains such as medical education. In this study, we examined the relationship between medical students’ (N = 30) outcome emotion profiles and their performance on a diagnostic reasoning task in the CBLE, BioWorld. We found that participants could be organized into distinct emotion groups using k-means cluster analyses based on their self-reported outcome emotion profiles: an expected positive emotion cluster and negative emotion cluster and an unexpected low intensity emotion cluster. A clear relationship was found between emotion clusters and diagnostic performance such that participants classified to the positive emotion cluster had the highest performance; those classified to the negative emotion cluster had the lowest performance; and those classified to the low intensity emotion cluster had performance outcomes that fell between the other two. Our discussion focuses on the theoretical implications of emotion classification and design recommendations for learning environments and emotional interventions in computer-based contexts.
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Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60, 383–398.
Arroyo, I., Muldner, K., Burleson, W., & Woolf, B. (2014). Adaptive interventions to address students’ negative activating and deactivating emotions during learning activities. Design Recommendations for Adaptive ITS (pp. 79–92). Adelphi, MD: U.S. Army Research Laboratory.
Artino, A. R., Jr., Hemmer, P. A., & Durning, S. J. (2011). Using self-regulated learning theory to understand the beliefs, emotions, and behaviors of struggling medical students. Academic Medicine, 86(10), S35–S38.
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., La Rochelle, J. S., & Durning, S. J. (2010). Second-year medical students’ motivational beliefs, emotions, and achievement. Medical Education, 44, 1203–1212.
Azevedo, R., Witherspoon, A., Chauncey, A., Burkett, C., & Fike, A. (2009). MetaTutor: A MetaCognitive tool for enhancing self-regulated learning. In R. Pirrone, R. Azevedo, & G. Biswas (Eds.), Proceedings of the AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems (pp. 14–19). Menlo Park, CA: Association for the Advancement of Artificial Intelligence (AAAI) Press.
Bearman, M. (2003). Is virtual the same as real? Medical students’ experiences of a virtual patient. Academic Medicine, 78(5), 538–545.
Beck, G. (2011). Investigation of the relationship between achievement emotions and academic performance in medical students. Unpublished doctoral dissertation. Capella University, Minneapolis, MN, USA.
Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach’s alpha. British Medical Journal, 314(7080), 572.
Boese, G. D., Stewart, T. L., Perry, R. P., & Hamm, J. M. (2013). Assisting failure-prone individuals to navigate achievement transitions using a cognitive motivation treatment (attributional retraining). Journal of Applied Social Psychology, 43(9), 1946–1955.
Bouchet, F., Harley, J. M., Trevors, G. J., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1), 104–146.
Carver, C. S., & Scheier, M. F. (2014). Dispositional optimism. Trends in Cognitive Sciences, 18, 293–299.
Courteille, O., Josephson, A., & Larsson, L. O. (2014). Interpersonal behaviors and socioemotional interaction of medical students in a virtual clinical encounter. BMC Medical Education, 14(1), 1.
Craig, S. D., 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 and Emotion, 22(5), 777–788.
D’Mello, S. K. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105(4), 1082–1099.
D’Mello, S. K., Craig, S. D., 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. K., & Graesser, A. C. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157.
D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.
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.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum-likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society B, 39, 1–38.
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, 401–426.
Dunphy, B. C., Cantwell, R., Bourke, S., Fleming, M., Smith, B., Joseph, K. S., et al. (2010). Cognitive elements in clinical decision-making. Advances in Health Sciences Education, 15(2), 229–250.
Gauthier, G., & Lajoie, S. P. (2014). Do expert clinical teachers have a shared understanding of what constitutes a competent reasoning performance in case-based teaching? Instructional Science, 42(4), 579–594.
Ghosh, D., & Vogt, A. (2012). Outliers: An evaluation of methodologies. In Joint Statistical Meetings (pp. 3455–3460). San Diego, CA: American Statistical Association.
Gross, J. J. (2013). Emotion regulation: Taking stock and moving forward. Emotion, 13(3), 359–365.
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., & Azevedo, R. (2014). Toward a feature-driven understanding of students’ emotions during interactions with agent-based learning environments: A selective review. International Journal of Games and Computer Mediated Simulation, 6(3), 17–34.
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 Computer Science: 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.
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.
Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N. C. (2016b). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International Journal of Artificial Intelligence in Education. doi:10.1007/s40593-016-0126-8.
Hartigan, J. A., & Wong, M. A. (1979). A K-means clustering algorithm. Applied Statistics, 28, 100–108.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.
Howell, D. C. (2013). Statistical methods for psychology. Belmont, CA: Wadsworth Cengage Learning.
Inventado, P. S., Legaspi, R., Suarez, M., & Numao, M. (2011). Predicting student emotions resulting from appraisal of ITS feedback. Research and Practice in Technology Enhanced Learning, 6(2), 107–133.
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.
Kernis, M. H., & Johnson, E. K. (1990). Current and typical self-appraisals: Differential responsiveness to evaluative feedback and implications for emotions. Journal of Research in Personality, 24(2), 241–257.
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, UK: Cambridge University Press.
Lajoie, S. P., & Azevedo, R. (2006). Teaching and learning in technology-rich environments. In P. Alexander & P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 803–821). Mahwah, NJ: Erlbaum.
Lajoie, S., Lee, L., Poitras, E., Bassiri, M., Kazemitabar, M., Cruz-Panesso, I., Hmelo-Silver, C., Wiseman, J., Chan, L., & Lu, J. (2015). The role of regulation in medical student learning in small groups: Regulating oneself and others’ learning and emotions. In Järvelä, S. & Hadwin, A. (Eds.) Special issue: Examining the emergence and outcomes of regulation in CSCL. Journal of Computer and Human Behavior.
Lazarus, R. S. (2006). Emotions and interpersonal relationships: Toward a person-centered conceptualization of emotions and coping. Journal of Personality, 74(1), 9–46.
Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty’s brain system. International Journal of Artificial Intelligence in Education, 18, 181–208.
Martinent, G., Nicolas, M., Gaudreau, P., & Campo, M. (2013). A cluster analysis of affective states before and during competition. Journal of Sport & Exercise Psychology, 35(6), 600–611.
McHugh, M. L. (2013). The Chi square test of independence. Biochemia Medica, 23(2), 143–149.
McQuiggan, S. W., Robison, J. L., & Lester, J. C. (2010). Affective transitions in narrative-centered learning environments. Educational Technology & Society, 13(1), 40–53.
Meyers, L., Gamst, G., & Guarino, A. (2013). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: SAGE.
Mooi, E., & Sarstedt, M. (2011). Cluster analysis (pp. 237–284). Berlin: Springer.
Naismith, L. M. (2013). Examining motivational and emotional influences on medical students’ attention to feedback in a technology-rich environment for learning clinical reasoning. Unpublished doctoral dissertation. Department of Educational and Counselling Psychology, McGill University, Montreal, Quebec, Canada.
Nielsen, P. A. (1991). Approaches to appreciate information systems methodologies: A soft system survey. Scandinavian Journal of Information Systems, 9, 43–60.
Pekrun, R. (2006). The control-value theory of achievement emotions. Educational Psychology Review, 18(4), 315–341.
Pekrun, R., & Hofmann, H. (1996). Affective and motivational processes: Contrasting interindividual and intraindividual perspectives. Paper presented at the annual meeting of the American Educational Research Association, New York.
Pekrun, R., & Perry P. P. (2014). Control-value theory of achievement emotions. In International handbook of emotions in education (pp. 120–141). Routledge: New York.
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance. Contemporary Educational Psychology, 36, 36–48.
Pekrun, R., Goetz, T., Perry, R. P., Kramer, K., Hochstadt, M., & Molfenter, S. (2004). Beyond test anxiety: Development and validation of the test emotions questionnaire (TEQ). Anxiety, Stress & Coping, 17, 287–316.
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105.
Perry, R. P., Stupnisky, R. H., Hall, N. C., Chipperfield, J. G., & Weiner, B. (2010). Bad starts and better finishes: Attributional retraining and initial performance in competitive achievement settings. Journal of Social and Clinical Psychology, 29(6), 668–700.
Peterson, E. R., Brown, G. T., & Jun, M. C. (2015). Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, 42, 82–96.
Pour, P. A., Hussain, M. S., AlZoubi, O., D’Mello, S., & Calvo, R. A. (2010). The impact of system feedback on learners’ affective and physiological states. In Intelligent tutoring systems (pp. 264–273). Berlin: Springer.
Rowe, A. D., Fitness, J., & Wood, L. N. (2014). The role and functionality of emotions in feedback at university: A qualitative study. The Australian Educational Researcher, 41(3), 283–309.
Rowe, J., Mott, B., McQuiggan, S., Robison, J., Lee, S., & Lester, J. (2009). Crystal island: A narrative-centered learning environment for eighth grade microbiology. In Workshop on intelligent educational games at the 14th international conference on artificial intelligence in education, Brighton (pp. 11–20).
Russell, 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.
Turner, J. C., Thorpe, P. K., & Meyer, D. K. (1998). Students’ reports of motivation and negative affect: A theoretical and empirical analysis. Journal of Educational Psychology, 90(4), 758.
Funding
This research was supported in part by a Master’s scholarship from SSHRC awarded to Amanda Jarrell, a Postdoctoral fellowship from FQRSC awarded to Dr. Jason Harley, grants from a SSHRC partnership (895-2011-1006), CRC (950-223504) and CF1 (30473) awarded to Dr. Susanne Lajoie, and doctoral fellowships from Richard H. Tomlinson (through McGill University) and SSHRC awarded to Dr. Laura Naismith.
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Dr. Jason Harley was a former students of Dr. Azevedo (ETR&D consulting editor). Dr. Azevedo was also a student of Dr. Susanne Lajoie, and is currently a co-investigator of an active grant with her. The authors declare that they have no other known conflicts of interest.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the McGill Research Ethics Board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Informed consent was obtained from all individual participants included in the study.
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Jarrell, A., Harley, J.M., Lajoie, S. et al. Success, failure and emotions: examining the relationship between performance feedback and emotions in diagnostic reasoning. Education Tech Research Dev 65, 1263–1284 (2017). https://doi.org/10.1007/s11423-017-9521-6
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DOI: https://doi.org/10.1007/s11423-017-9521-6