Journal of Computers in Education

, Volume 3, Issue 3, pp 289–307

The link between achievement emotions, appraisals, and task performance: pedagogical considerations for emotions in CBLEs

  • Amanda Jarrell
  • Jason M. Harley
  • Susanne P. Lajoie
Article

Abstract

Achievement emotions have a powerful influence on how students interact with current and future learning and performance tasks. As such, pedagogical practices that support adaptive student emotions are critical for teaching and learning in computer-based learning environments (CBLEs). This research investigates the relationship between during-task achievement emotions and participants’ appraisals of task control, value, perceived performance, and actual performance outcomes on a diagnostic reasoning task with a CBLE, BioWorld. Based on the emotions participants reported experiencing during the task, we found that participants could be organized into three groups using a k-means cluster analysis: a positive, negative, and low emotion group. Participants assigned to the positive emotion group had the highest subjective appraisals of task value, task control, and the highest perceived performance; however, these participants had lower levels of actual performance when compared to learners assigned to the low emotion cluster and had actual performance levels comparable to learners in the negative emotion cluster. These results provide preliminary evidence for fostering low emotionality rather than positive emotionality with pedagogical interventions in order to support better performance outcomes, while learners engage in academic achievement tasks in CBLEs.

Keywords

Emotions Affect Performance Computer-based learning environments Problem solving Medical decision making 

References

  1. Baker, R. S. J., D’Mello, S. K., Mercedes, 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.CrossRefGoogle Scholar
  2. Bosch, N., D’Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015). Automatic detection of learning-centered affective states in the wild. In Proceedings of the 20th international conference on intelligent user interfaces (pp. 379–388). ACM.Google Scholar
  3. Calvo, R. A., & D’Mello, A. C. (2012). Frontiers of affect-aware learning technologies. IEEE Intelligent Systems, 27, 86–89.CrossRefGoogle Scholar
  4. Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. Affective Computing, IEEE Transactions on, 1(1), 18–37.CrossRefGoogle Scholar
  5. Carver, C. S., & Scheier, M. F. (2014). The experience of emotions during goal pursuit. In R. Pekrun & L. Linnenbrink- Garcia (Eds.), International handbook of emotions in education (pp. 56–72). New York: Routledge.Google Scholar
  6. Cassady, J. C. (2004). The influence of cognitive test anxiety across the learning–testing cycle. Learning and Instruction, 14(6), 569–592.CrossRefGoogle Scholar
  7. 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, 1082–1099.CrossRefGoogle Scholar
  8. D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157.CrossRefGoogle Scholar
  9. D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. Doleck, T., Basnet, R. B., Poitras, E. G., & Lajoie, S. P. (2015). Mining learner–system interaction data: implications for modeling learner behaviors and improving overlay models. Journal of Computers in Education, 2(4), 421–447.CrossRefGoogle Scholar
  12. Duffy, M., Lajoie, S., Jarrell, A., Pekrun, R., Azevedo, R., & Lachapelle, K. (2015). Emotions in medical education: Developing and testing a scale of emotions across medical learning environments. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL.Google Scholar
  13. Frenzel, A. C., Thrash, T. M., Pekrun, R., & Goetz, T. (2007). Achievement emotions in Germany and China: A cross-cultural validation of the Academic Emotions Questionnaire-Mathematics (AEQ-M). Journal of Cross-Cultural Psychology, 38, 302–309.CrossRefGoogle Scholar
  14. Goetz, T., Frenzel, A. C., Stoeger, H., & Hall, N. C. (2010). Antecedents of everyday positive emotions: An experience sampling analysis. Motivation and Emotion, 34(1), 49–62.CrossRefGoogle Scholar
  15. Hall, N. C., Perry, R. P., Ruthig, J. C., Hladkyj, S., & Chipperfield, J. G. (2006). Primary and secondary control in achievement settings: A longitudinal field study of academic motivation, emotions, and performance. Journal of Applied Social Psychology, 36(6), 1430–1470.CrossRefGoogle Scholar
  16. 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 Gaming and Computer-Mediated Simulation, 6(3), 17–34.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Harley, J. M., Carter, C. K., Papaionnou, N., Bouchet, F., Azevedo, R., Landis, R. L., & Karabachian, L. (2016). 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.Google Scholar
  19. Jarrell, A., Harley, J. M., Lajoie, S. P., & Naismith, L. (2015). Examining the relationship between performance feedback and emotions in diagnostic reasoning: Toward a predictive framework for emotional support. In C. Conati & N. Heffernan (Eds.), Lectures notes in artificial intelligence (Vol. 9112, pp. 657–660). Artificial intelligence in education Switzerland: Springer.Google Scholar
  20. Jarrell, A., Harely, J. M., & Lajoie, S. P. (2016). How do emotions experienced during problem solving relate to students’ perceived and actual performance? Paper presented at the American Educational Research Association, Washington, DC.Google Scholar
  21. Lajoie, S. P. (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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Lazarus, R. S. (2006). Emotions and interpersonal relationships: Toward a person-centered conceptualization of emotions and coping. Journal of Personality, 74(1), 9–46.CrossRefGoogle Scholar
  24. Linnenbrink-Garcia, L., & Barger, M. M. (2014). Achievement goals and emotions. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 142–161). New York: Routledge.Google Scholar
  25. 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.CrossRefGoogle Scholar
  26. Mayer, R. (2011). Information processing. In K. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook: Theories, constructs, and critical issues (Vol. 1, pp. 85–100). Washington, DC: APA.Google Scholar
  27. Meinhardt, J., & Pekrun, R. (2003). Attentional resource allocation to emotional events: An ERP study. Cognition and Emotion, 17, 477–500.CrossRefGoogle Scholar
  28. Meyers, L., Gamst, G., & Guarino, A. (2013). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: SAGE.Google Scholar
  29. 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, QC, Canada.Google Scholar
  30. 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
  31. 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), 115–135.CrossRefGoogle Scholar
  32. Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. 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. doi:10.1037/a0019243.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.Google Scholar
  35. 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
  36. Ranellucci, J., Hall, N. C., & Goetz, T. (2015). Achievement goals, emotions, learning, and performance: A process model. Motivation Science, 1(2), 98–120.CrossRefGoogle Scholar
  37. Rieber, L. P., & Noah, D. (2008). Games, simulations, and visual metaphors in education: antagonism between enjoyment and learning. Educational Media International, 45(2), 77–92.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. Ruthig, J. C., Perry, R. P., Hladkyj, S., Hall, N. C., Pekrun, R., & Chipperfield, J. G. (2008). Perceived control and emotions: Interactive effects on performance in achievement settings. Social Psychology of Education, 11(2), 161–180.CrossRefGoogle Scholar
  40. Seibert, P. S., & Ellis, H. C. (1991). Irrelevant thoughts, emotional mood states, and cognitive task performance. Memory & Cognition, 19(5), 507–513.CrossRefGoogle Scholar
  41. Tong, E. M., Bishop, G. D., Enkelmann, H. C., Why, Y. P., Diong, S. M., Khader, M., & Ang, J. (2007). Emotion and appraisal: A study using ecological momentary assessment. Cognition and Emotion, 21(7), 1361–1381.CrossRefGoogle Scholar
  42. Zeidner, M. (1998). Test anxiety: The state of the art. New York: Springer.Google Scholar
  43. Zeidner, M. (2014). Anxiety in education. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 265–288). New York: Taylor & Francis.Google Scholar
  44. Zimmerman, B. J., & Labuhn, A. S. (2012). Self-regulation of learning: Process approaches to personal development. In K. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook: Theories, constructs, and critical issues (Vol. 1, pp. 399–425). Washington, DC: APA.CrossRefGoogle Scholar

Copyright information

© Beijing Normal University 2016

Authors and Affiliations

  • Amanda Jarrell
    • 1
  • Jason M. Harley
    • 2
  • Susanne P. Lajoie
    • 1
  1. 1.Department of Educational and Counselling PsychologyMcGill UniversityMontréalCanada
  2. 2.Department of Educational PsychologyUniversity of AlbertaEdmontonCanada

Personalised recommendations