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Instructional Science

, Volume 40, Issue 5, pp 813–827 | Cite as

Conveying clinical reasoning based on visual observation via eye-movement modelling examples

  • Halszka JarodzkaEmail author
  • Thomas Balslev
  • Kenneth Holmqvist
  • Marcus Nyström
  • Katharina Scheiter
  • Peter Gerjets
  • Berit Eika
Article

Abstract

Complex perceptual tasks, like clinical reasoning based on visual observations of patients, require not only conceptual knowledge about diagnostic classes but also the skills to visually search for symptoms and interpret these observations. However, medical education so far has focused very little on how visual observation skills can be efficiently conveyed to novices. The current study applied a novel instructional method to teach these skills by showing the learners how an expert model visually searches and interprets symptoms (i.e., eye-movement modelling examples; EMMEs). Case videos of patients were verbally explained by a model (control condition) and presented to students. In the experimental conditions, the participants received a recording of the model’s eye movements superimposed on the case videos. The eye movements were displayed by either highlighting the features the model focused on with a circle (the circle condition) or by blurring the features the model did not focus on (the spotlight condition). Compared to the other two conditions, results show that a spotlight on the case videos better guides the students’ attention towards the relevant features. Moreover, when testing the students’ clinical reasoning skills with videos of new patient cases without any guidance, participants studying EMMEs with a spotlight showed improved their visual search and enhanced interpretation performance of the symptoms in contrast to participants in either the circle or the control condition. These findings show that a spotlight EMME can successfully convey clinical reasoning based on visual observations.

Keywords

Example-based learning Eye tracking Expertise Attention Medical education 

References

  1. Antes, J. R., & Kristjanson, A. F. (1991). Discriminating artists from nonartists by their eye-fixation patterns. Perceptual and Motor Skills, 73, 893–894.Google Scholar
  2. Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70, 181–214.Google Scholar
  3. Balslev, T., De Grave, W. S., Muijtjens, A. M. M., Eika, B., & Scherpbier, A. J. J. A. (2009). The development of shared cognition in paediatric residents analysing a patient video case versus a paper patient case. Advances in Health Science Education, 14, 557–565.CrossRefGoogle Scholar
  4. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  5. Boshuizen, H. P. A., & Schmidt, H. G. (1992). On the role of biomedical knowledge in clinical reasoning by experts, intermediates, and novices. Cognitive Science, 16, 153–184.CrossRefGoogle Scholar
  6. Brooks, L. R., LeBlanc, V. R., & Norman, G. R. (2000). On the difficulty of noticing obvious features in patient appearance. Psychological Science, 11, 112–117.CrossRefGoogle Scholar
  7. Charness, N., Reingold, E. M., Pomplun, M., & Stampe, D. (2001). The perceptual aspect of skilled performance in chess: Evidence from eye movements. Memory and Cognition, 29, 1146–1152.CrossRefGoogle Scholar
  8. Chi, M. T. H. (2006). Laboratory methods for assessing experts’ and novices’ knowledge. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 167–184). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  9. Collins, A. F., Brown, J. S., & Newman, S. (1989). Cognitive apprenticeship: Teaching the craft of reading, writing, and mathematics. In L. Resnick (Ed.), Cognition and instruction: Issues and agendas (pp. 453–494). Mahwah, NJ: Erlbaum.Google Scholar
  10. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychology Review, 21, 113–140.CrossRefGoogle Scholar
  11. De Leng, B. A., Dolmans, D. H. J. M., Van de Wiel, M., Muijtjens, A. M. M., & Van der Vleuten, C. P. M. (2007). How video cases should be used as authentic stimuli in problem-based medical education. Medical Education, 41, 181–188.CrossRefGoogle Scholar
  12. Dequeker, J., & Jaspaert, R. (1998). Teaching problem-solving and clinical reasoning: 20 years experience with video-supported small-group learning. Medical Education, 32, 384–389.CrossRefGoogle Scholar
  13. Dorr, M., Jarodzka, H., & Barth, E. (2010). Space-variant spatio-temporal filtering of video for gaze visualization and perceptual learning. In C. Morimoto & H. Instance (Eds.), Proceedings of eye tracking research & applications ETRA’10 (pp. 307–314). New York, NY: ACM.Google Scholar
  14. Egger, J., Grossmann, G., & Auchterlonie, I. A. (2003). Benign sleep myoclonus in infancy mistaken for epilepsy. British Medical Journal, 326, 975–976.CrossRefGoogle Scholar
  15. Grant, E. R., & Spivey, M. J. (2003). Eye movements and problem solving: Guiding attention guides thought. Psychological Science, 14, 462–466.CrossRefGoogle Scholar
  16. Hansen, J. K., & Balslev, T. (2009). Hand activities in infantile masturbation: A video analysis of 13 cases. European Journal of Paediatric Neurology, 13, 508–510.CrossRefGoogle Scholar
  17. Helle, L., Nivala, M., Kronqvist, P., Gegenfurtner, A., Björk, P., & Säljö, R. (2011). Traditional microscopy instruction versus process-oriented virtual microscopy instruction: A naturalistic experiment with control group. Diagnostic Pathology, 6(Suppl 1), S8.CrossRefGoogle Scholar
  18. Hinds, P. I. (1999). The curse of expertise: The effects of expertise and debiasing methods on predictions of novice performance. Journal of Experimental Psychology Applied, 5, 205–221.CrossRefGoogle Scholar
  19. International League Against Epilepsy. (2010). Revised terminology and concepts for organization of the epilepsies: Report of the commission on classification and terminology. Epilepsia, 51, 676–685.CrossRefGoogle Scholar
  20. Jarodzka, H., Scheiter, K., Gerjets, P., & Van Gog, T. (2010a). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20, 146–154.CrossRefGoogle Scholar
  21. Jarodzka, H., Van Gog, T., Dorr, M., Scheiter, K., & Gerjets, P. (2010b). How to convey perceptual skills by displaying experts’ gaze data. In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st annual conference of the cognitive science society (pp. 2920–2925). Austin, TX: Cognitive Science Society.Google Scholar
  22. Jucks, R., Schulte-Löbbert, P., & Bromme, R. (2007). Supporting experts’ written knowledge communication through reflective prompts on the use of specialist concepts. Journal of Psychology, 215, 237–247.Google Scholar
  23. Kamin, C., O’Sullivan, P., Deterding, R., & Younger, M. (2003). A comparison of critical thinking in groups of third-year medical students in text, video, and virtual PBL case modalities. Academic Medicine, 78, 204–211.CrossRefGoogle Scholar
  24. Krupinski, E. A. (2005). Visual search of mammographic images: Influence of lesion subtlety. Academic Radiology, 12, 965–969.CrossRefGoogle Scholar
  25. Krupinski, E. A. (2010). Current perspectives in medical image perception. Attention, Perception, & Psychophysics, 72, 1205–1217.CrossRefGoogle Scholar
  26. Krupinski, E. A., Tillack, A. A., Richter, L., Henderson, J. T., Bhattacharyya, A. K., Scott, K. M., et al. (2006). Eye-movement study and human performance using telepathology virtual slides: Implications for medical education and differences with experience. Human Pathology, 37, 1543–1556.CrossRefGoogle Scholar
  27. Kundel, H., Nodine, C., Krupinski, E., & Mello-Thoms, C. (2008). Using gaze-tracking data and mixture distribution analysis to support a holistic model for the detection of cancers on mammograms. Academic Radiology, 15, 881–886.CrossRefGoogle Scholar
  28. Lesgold, A., Rubinson, H., Feltovich, P., Glaser, R., Klopfer, D., & Wang, Y. (1988). Expertise in a complex skill: Diagnosing X-ray pictures. In M. T. H. Chi, R. Glaser, & M. Farr (Eds.), The nature of expertise (pp. 311–342). Hillsdale, NJ: Erlbaum.Google Scholar
  29. Litchfield, D., Ball, L. J., Donovan, T., Manning, D. J., & Crawford, T. (2010). Viewing another person’s eye movements improves identification of pulmonary nodules in chest X-ray inspection. Journal of Experimental Psychology: Applied, 16, 251–262.CrossRefGoogle Scholar
  30. Lowe, R. K. (1999). Extracting information from an animation during complex visual learning. European Journal of Psychology of Education, 14, 225–244.CrossRefGoogle Scholar
  31. Lüders, H., Acharya, J., Baumgartner, C., Benbadis, S., Bleasel, A., Burgess, R., et al. (1998). Semiological seizure classification. Epilepsia, 39, 1006–1013.CrossRefGoogle Scholar
  32. Nordli, D. (2002). Infantile seizures and epilepsy syndromes. Epilepsia, 43, 11–16.CrossRefGoogle Scholar
  33. Nordli, D., Bazil, C. W., Scheuer, M. L., & Pedley, T. A. (1997). Recognition and classification of seizures in infants. Epilepsia, 38, 553–560.CrossRefGoogle Scholar
  34. Nückles, M., Winter, A., Wittwer, J., Herbert, M., & Hübner, S. (2006). How do experts adapt their explanations to a layperson’s knowledge in asynchronous communication? An experimental study. User Modeling and User Adapted Interaction, 16, 87–127.CrossRefGoogle Scholar
  35. Nyström, M. (2008). Off-line foveated compression and scene perception: An eye tracking approach. Unpublished doctoral dissertation, Lund University, Lund.Google Scholar
  36. Nyström, M., & Holmqvist, K. (2008). Semantic override of low-level features in image viewing—both initially and overall. Journal of Eye Movement Research, 2, 1–11.Google Scholar
  37. Rao, R. P. N., Zielinsky, G. J., Hayhoe, M. M., & Ballard, D. H. (2002). Eye movements in iconic visual search. Vision Research, 42, 1447–1463.CrossRefGoogle Scholar
  38. Schmidt, D., & Schachter, S. C. (2000). Epilepsy: Problem solving in clinical practice. London, UK: Martin Dunitz.Google Scholar
  39. Simon, H. A. (1983). Why should machines learn? In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 25–38). Palo Alto, CA: Tioga.Google Scholar
  40. Sweller, J., Van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychological Review, 10, 251–296.CrossRefGoogle Scholar
  41. Underwood, G., Chapman, P., Brocklehurst, N., Underwood, J., & Crundall, D. (2003). Visual attention while driving: Sequences of eye fixations made by experienced and novice drivers. Ergonomics, 46, 629–646.CrossRefGoogle Scholar
  42. Van Gog, T., Jarodzka, H., Scheiter, K., Gerjets, P., & Paas, F. (2009). Attention guidance during example study via the model’s eye movements. Computers in Human Behavior, 25, 785–791.CrossRefGoogle Scholar
  43. Van Gog, T., Paas, F., & Van Merriënboer, J. J. G. (2006). Effects of process-oriented worked examples on troubleshooting transfer performance. Learning and Instruction, 16, 154–164.CrossRefGoogle Scholar
  44. Van Lehn, K. (1989). Problem solving and cognitive skill acquisition. In M. Posner (Ed.), Foundations of cognitive science (pp. 527–579). Mahwah, NJ: Erlbaum.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Halszka Jarodzka
    • 1
    Email author
  • Thomas Balslev
    • 2
  • Kenneth Holmqvist
    • 3
  • Marcus Nyström
    • 3
  • Katharina Scheiter
    • 4
  • Peter Gerjets
    • 4
  • Berit Eika
    • 5
  1. 1.Center of Learning Sciences and TechnologiesOpen University of The NetherlandsHeerlenThe Netherlands
  2. 2.Department of PediatricsViborg Regional HospitalViborgDenmark
  3. 3.Humanities LabLund UniversityLundSweden
  4. 4.Knowledge Media Research CenterTuebingenGermany
  5. 5.Center of Medical EducationAarhus UniversityAarhusDenmark

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