Advances in Health Sciences Education

, Volume 23, Issue 3, pp 465–485 | Cite as

Motivation and emotion predict medical students’ attention to computer-based feedback

  • Laura M. NaismithEmail author
  • Susanne P. Lajoie


Students cannot learn from feedback unless they pay attention to it. This study investigated relationships between the personal factors of achievement goal orientations, achievement emotions, and attention to feedback in BioWorld, a computer environment for learning clinical reasoning. Novice medical students (N = 28) completed questionnaires to measure their achievement goal orientations and then thought aloud while solving three endocrinology patient cases and reviewing corresponding expert solutions. Questionnaires administered after each case measured participants’ experiences of five feedback emotions: pride, relief, joy, shame, and anger. Attention to individual text segments of the expert solutions was modelled using logistic regression and the method of generalized estimating equations. Participants did not attend to all of the feedback that was available to them. Performance-avoidance goals and shame positively predicted attention to feedback, and performance-approach goals and relief negatively predicted attention to feedback. Aspects of how the feedback was displayed also influenced participants’ attention. Findings are discussed in terms of their implications for educational theory as well as the design and use of computer learning environments in medical education.


Achievement emotions Achievement goal orientations Attention Clinical reasoning Computer learning environments Feedback Undergraduate medical education 



This work was funded in part by grants awarded to Susanne P. Lajoie from McGill University and the Social Sciences and Humanities Research Council of Canada (Grant No. 410-2008-1117). Laura M. Naismith received doctoral fellowships from Richard H. Tomlinson (through McGill University) and the Social Sciences and Humanities Research Council of Canada. The authors wish to acknowledge Maedeh Kazemitabar and Cynthia Psaradellis for their assistance with data collection and Robert Bracewell for his assistance with the statistical analysis.


  1. Allison, P. D. (1999). Logistic regression using the SAS ® system: Theory and application. Cary, NC: SAS Institute Inc.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, 240–244.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. Bearman, M. (2003). Is virtual the same as real? Medical students’ experiences of a virtual patient. Academic Medicine, 78(5), 538–545.CrossRefGoogle Scholar
  5. Berman, N. B., Durning, S. J., Fischer, M. R., Huwendiek, S., & Triola, M. M. (2016). The role for virtual patients in the future of medical education. Academic Medicine, 91(9), 1217–1222.CrossRefGoogle Scholar
  6. Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical Care, 9(1), 112–118.CrossRefGoogle Scholar
  7. Botezatu, M., Hult, H., & Fors, U. G. (2010). Virtual patient simulation: What do students make of it? A focus group study. BMC Medical Education, 10, 91.CrossRefGoogle Scholar
  8. Bynum, W. E., 4th, & Goodie, J. L. (2014). Shame, guilt, and the medical learner: Ignored connections and why we should care. Medical Education, 48(11), 1045–1054.CrossRefGoogle Scholar
  9. Calcott, R. B., & Berkman, E. T. (2014). Attentional flexibility during approach and avoidance motivational states: The role of context in shifts of attentional breadth. Journal of Experimental Psychology: General, 143(3), 1393–1408.CrossRefGoogle Scholar
  10. Cândea, D. M., & Szentágotai-Tătar, A. (2017). Shame as a predictor of post-event rumination in social anxiety. Cognition and Emotion, 31(8), 1684–1691.CrossRefGoogle Scholar
  11. Chen, H. C., ten Cate, O., O’Sullivan, P., Boscardin, C., Eidson-Ton, W. S., Basaviah, P., et al. (2016). Students’ goal orientations, perceptions of early clinical experiences and learning outcomes. Medical Education, 50(2), 203–213.CrossRefGoogle Scholar
  12. Chi, M. T. H. (1997). Quantifying qualitatative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6(3), 271–315.CrossRefGoogle Scholar
  13. Conati, C., Jaques, N., & Muir, M. (2013). Understanding attention to adaptive hints in educational games: An eye-tracking study. International Journal of Artificial Intelligence in Education, 23(1–4), 136–161.CrossRefGoogle Scholar
  14. Cook, D. A., Erwin, P. J., & Triola, M. M. (2010). Computerized virtual patients in health professions education: A systematic review and meta-analysis. Academic Medicine, 85(10), 1589–1602.CrossRefGoogle Scholar
  15. Critchley, L. A. H., Kumta, S. M., Ware, J., & Wong, J. W. (2009). Web-based formative assessment case studies: Role in a final year medicine two-week anaesthesia course. Anaesthesia and Intensive Care, 37(4), 637–645.Google Scholar
  16. D’Mello, S. K., Lehman, B., & Person, N. (2010). Monitoring affect states during effortful problem solving activities. International Journal of Artificial Intelligence in Education, 20, 361–389.Google Scholar
  17. Daniels, L. M., Stupnisky, R. H., Pekrun, R., Haynes, T. L., Perry, R. P., & Newell, 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
  18. DeShon, R. P., & Gillespie, J. Z. (2005). A motivated action theory account of goal orientation. Journal of Applied Psychology, 90(6), 1096–1127.CrossRefGoogle Scholar
  19. Dunlosky, J., Hartwig, M. K., Rawson, K. A., & Lipko, A. R. (2011). Improving college students’ evaluation of text learning using idea-unit standards. The Quarterly Journal of Experimental Psychology, 64(3), 467–484.CrossRefGoogle Scholar
  20. 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, 229–250.CrossRefGoogle Scholar
  21. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048.CrossRefGoogle Scholar
  22. Elliot, A. J. (2005). A conceptual history of the achievement goal construct. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 52–72). New York: Guilford Press.Google Scholar
  23. Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72, 218–232.CrossRefGoogle Scholar
  24. Elliot, A. J., & Thrash, T. M. (2001). Achievement goals and the hierarchical model of achievement motivation. Educational Psychology Review, 13(2), 139–156.CrossRefGoogle Scholar
  25. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.Google Scholar
  26. Eva, K. W., Armson, H., Holmboe, E., Lockyer, J., Loney, E., Mann, K., et al. (2012). Factors influencing responsiveness to feedback: On the interplay between fear, confidence, and reasoning processes. Advances in Health Sciences Education, 17, 15–26.CrossRefGoogle Scholar
  27. Evensen, D. H., Salisbury-Glennon, J. D., & Glenn, J. (2001). A qualitative study of six medical students in a problem-based curriculum: Toward a situated model of self-regulation. Journal of Educational Psychology, 93(4), 659–676.CrossRefGoogle Scholar
  28. Frederiksen, C., & Bracewell, R. (2012). Statistical treatment of qualitative/categorical data in the learning sciences [PowerPoint slides]. Montreal: Presentation given at the Learning Sciences Research Seminar, McGill University.Google Scholar
  29. Gauthier, G., Naismith, L., Lajoie, S. P., & Wiseman, J. (2008). Using expert decision maps to promote reflection and self-assessment in medical case-based instruction. In V. Aleven, K. Ashley, C. Lynch, & N. Pinkwart (Chairs), Intelligent tutoring systems for ill-defined domains (pp. 68–80). Workshop conducted at the 9th International Conference on Intelligent Tutoring Systems, Montreal, Canada.Google Scholar
  30. Ghisletta, P., & Spini, D. (2004). An introduction to generalized estimating equations and an application to assess selectivity effects in a longitudinal study on very old individuals. Journal of Educational and Behavioral Statistics, 29(4), 421–437.CrossRefGoogle Scholar
  31. Harrison, C. J., Könings, K. D., Molyneux, A., Schuwirth, L., Wass, V., & van der Vleuten, C. (2015). Barriers to the uptake and use of feedback in the context of summative assessment. Advances in Health Sciences Education, 20, 229–245.CrossRefGoogle Scholar
  32. Harrison, C. J., Könings, K. D., Molyneux, A., Schuwirth, L. W. T., Wass, V., & van der Vleuten, C. P. M. (2013). Web-based feedback after summative assessment: How do students engage? Medical Education, 47, 734–744.CrossRefGoogle Scholar
  33. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.CrossRefGoogle Scholar
  34. Hautz, W. E., Schröder, T., Dannenberg, K. A., März, M., Hölzer, H., Ahlers, O., et al. (2017). Shame in medical education: A randomized study of the acquisition of intimate examination skills and its effect on subsequent performance. Teaching and Learning in Medicine, 29(2), 196–206.CrossRefGoogle Scholar
  35. Helmich, E., Bolhuis, S., Dornan, T., Laan, R., & Koopmans, R. (2012). Entering medical practice for the very first time: Emotional talk, meaning and identity development. Medical Education, 46, 1074–1186.CrossRefGoogle Scholar
  36. Horowitz, G. (2010). It’s not always just about the grade: Exploring the achievement goal orientations of pre-med students. The Journal of Experimental Education, 78, 215–245.CrossRefGoogle Scholar
  37. Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136(3), 422–449.CrossRefGoogle Scholar
  38. Huwendiek, S., Reichert, G., Bosse, H.-M., de Leng, B. A., van der Vleuten, C. P. M., Haag, M., et al. (2009). Design principles for virtual patients: A focus group study among students. Medical Education, 43, 580–588.CrossRefGoogle Scholar
  39. Immordino-Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1(1), 3–10.CrossRefGoogle Scholar
  40. Janssen, O., & Prins, J. (2007). Goal orientations and the seeking of different types of feedback information. Journal of Occupational and Organizational Psychology, 80, 235–249.CrossRefGoogle Scholar
  41. 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
  42. Lajoie, S. P., & Azevedo, R. (2006). Teaching and learning in technology-rich environments. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 803–821). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  43. Lajoie, S. P., Naismith, L., Poitras, E., Hong, Y.-J., Cruz-Panesso, I., Ranellucci, J., et al. (2013). Technology-rich tools to support self-regulated learning and performance in medicine. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 229–242). New York: Springer Science.CrossRefGoogle Scholar
  44. LeBlanc, V. R., McConnell, M. M., & Monteiro, S. (2015). Predictable chaos: A review of the effects of emotions on attention, memory and decision making. Advances in Health Sciences Education, 20, 265–282.CrossRefGoogle Scholar
  45. Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22.CrossRefGoogle Scholar
  46. Linnenbrink-Garcia, L., Middleton, M. J., Ciani, K. D., Easter, M. A., O’Keefe, P. A., & Zusho, A. (2012). The strength of the relation between performance-approach and performance-avoidance goal orientations: Theoretical, methodological, and instructional implications. Educational Psychologist, 47(4), 281–301.CrossRefGoogle Scholar
  47. Madjar, N., Kushnir, T., & Bachner, Y. G. (2015). Communication skills training in medical students: Do motivational orientation predict changes over time in psychosocial attributes? Advances in Health Sciences Education, 20(1), 45–57.CrossRefGoogle Scholar
  48. Mason, B. J., & Bruning, R. (2001). Providing feedback in computer-based instruction: What the research tells us. CLASS Research Report No. 9. Center for Instructional Innovation, University of Nebraska-Lincoln.Google Scholar
  49. Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion : A review. Cognition and Emotion, 23(2), 209–237.CrossRefGoogle Scholar
  50. McConnell, M. M., & Eva, K. W. (2012). The role of emotion in the learning and transfer of clinical skills and knowledge. Academic Medicine, 87(10), 1316–1322.CrossRefGoogle Scholar
  51. McConnell, M. M., & Shore, D. I. (2011). Upbeat and happy: Arousal as an important factor in studying attention. Cognition and Emotion, 25(7), 1184–1195.CrossRefGoogle Scholar
  52. Merriman, K. K., Clariana, R. B., & Bernardi, R. J. (2012). Goal orientation and feedback congruence: Effects on discretionary effort and achievement. Journal of Applied Social Psychology, 42(11), 2776–2796.CrossRefGoogle Scholar
  53. Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., et al. (2000). Manual for the patterns of adaptive learning scales. Ann Arbor: University of Michigan.Google Scholar
  54. Murayama, K., Elliot, A., & Yamagata, S. (2011). Separation of performance-approach and performance-avoidance achievement goals: A broader analysis. Journal of Educational Psychology, 103(1), 238–256.CrossRefGoogle Scholar
  55. Naismith, L., & Lajoie, S. P. (2010). Using expert models to provide feedback on clinical reasoning skills. In V. Aleven, J. Kay, & J. Mostow (Eds.), Proceedings of the 10th international conference on intelligent tutoring systems, LNCS 6095 (pp. 242–244). Berlin: Springer.Google Scholar
  56. Payne, S. C., Youngcourt, S. S., & Beaubien, J. M. (2007). A meta-analytic examination of the goal orientation nomological net. Journal of Applied Psychology, 92(1), 128–150.CrossRefGoogle Scholar
  57. Pekrun, R. (2000). A social cognitive, control-value theory of achievement emotions. In J. Heckhausen (Ed.), Motivational psychology of human development (pp. 143–163). Oxford: Elsevier.Google Scholar
  58. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.CrossRefGoogle Scholar
  59. Pekrun, R., Cusack, A., Murayama, K., Elliot, A. J., & Thomas, K. (2014). The power of anticipated feedback: Effects on students’ achievement goals and achievement emotions. Learning and Instruction, 29, 115–124.CrossRefGoogle Scholar
  60. 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
  61. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48.CrossRefGoogle Scholar
  62. 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.CrossRefGoogle Scholar
  63. Pekrun, R., & Perry, P. 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
  64. Poulos, A., & Mahony, M. J. (2008). Effectiveness of feedback: The student’s perspective. Assessment and Evaluation in Higher Education, 33(2), 143–154.CrossRefGoogle Scholar
  65. Sargeant, J., Mann, K., Sinclair, D., van der Vleuten, C., & Metsemakers, J. (2008). Understanding the influence of emotions and reflection upon multi-source feedback acceptance and use. Advances in Health Sciences Education, 13, 275–288.CrossRefGoogle Scholar
  66. Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189.CrossRefGoogle Scholar
  67. Simon, H. A., & Kaplan, C. A. (1989). Foundations of cognitive science. In M. E. Posner (Ed.), Foundations of cognitive science (pp. 1–47). Cambridge, MA: MIT Press.Google Scholar
  68. So, Y. (2008). The effects of achievement goal orientation and self-efficacy on course interests and academic achievement in medical students. Korean Journal of Medical Education, 20(1), 37–49.CrossRefGoogle Scholar
  69. Song, H. S. (2010). The effects of learners’ prior knowledge, self-regulation, and motivation on learning performance in complex multimedia learning environments. Unpublished doctoral dissertation, New York University.Google Scholar
  70. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Pearson Education Inc.Google Scholar
  71. Telio, S., Ajjawi, R., & Regehr, G. (2015). The “educational alliance” as a framework for reconceptualising feedback in medical education. Academic Medicine, 90(5), 609–614.CrossRefGoogle Scholar
  72. Teunissen, P. W., Stapel, D. A., van der Vleuten, C., Scherpbier, A., Bloor, K., & Scheele, F. (2009). Who wants feedback? An investigation of the variables influencing residents’ feedback-seeking behavior in relation to night shifts. Academic Medicine, 84(7), 910–917.CrossRefGoogle Scholar
  73. van de Ridder, J. M., McGaghie, W. C., Stokking, K. M., & ten Cate, O. T. (2015). Variables that affect the process and outcome of feedback, relevant for medical training: A meta-review. Medical Education, 49(7), 658–673.CrossRefGoogle Scholar
  74. Velan, G. M., Killen, M. T., Dziegielewski, M., & Kumar, R. K. (2002). Development and evaluation of a computer-assisted learning module on glomerulonephritis for medical students. Medical Teacher, 24(4), 412–416.CrossRefGoogle Scholar
  75. Wahlgren, C.-F., Edelbring, S., Fors, U., Hindbeck, H., & Stahle, M. (2006). Evaluation of an interactive case simulation system in dermatology and venereology for medical students. BMC Medical Education, 6, 40.CrossRefGoogle Scholar
  76. Walker, E., Rummel, N., Walker, S., & Koedinger, K. R. (2012). Noticing relevant feedback improves learning in an intelligent tutoring system for peer tutoring. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 222–232). Berlin: Springer-Verlag.Google Scholar
  77. Watling, C., Driessen, E., van der Vleuten, C. P., & Lingard, L. (2012). Learning from clinical work: The roles of learning cues and credibility judgements. Medical Education, 46(2), 192–200.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Centre for Addiction and Mental HealthTorontoCanada
  2. 2.Department of Educational and Counselling PsychologyMcGill UniversityMontrealCanada

Personalised recommendations