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Using virtual microscopy to scaffold learning of pathology: a naturalistic experiment on the role of visual and conceptual cues


New representational technologies, such as virtual microscopy, create new affordances for medical education. In the article, a study on the following two issues is reported: (a) How does collaborative use of virtual microscopy shape students’ engagement with and learning from virtual slides of tissue specimen? (b) How do visual and conceptual cues scaffold students’ reasoning? Fifteen pairs of medical students participated in two sessions in which the students used a virtual microscope as a diagnostic tool in the context of learning pathology. The slides provided the students with varying levels of visual and conceptual cueing. The sessions were videotaped, and the students’ reasoning while using the microscope was analysed. The students’ written answers were analysed in terms of the findings they made and the diagnoses suggested. At a general level, the results show that students engage actively in this kind of virtually-mediated environment. The visual and/or conceptual cues improve students’ performance, and guide the students’ perception and reasoning in a manner that is productive from the point of view of learning to make clinically relevant observations. Scaffolding students’ reasoning process through cues furthermore assists the students in avoiding the most obvious pitfalls such as overlooking critical areas of a specimen. Overall, visual and conceptual cues improve students’ reasoning in perceptual and cognitive terms, while still allowing space for the making of “relevant mistakes” that may further the students’ diagnostic skills.

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  1. 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.

  2. Berbaum, K. S., Franken, E. A., Dorfman, D. D., Rooholamini, S. A., Kathol, M. H., Barloon, T. J., et al. (1990). Satisfaction of search in diagnostic radiology. Investigative Radiology, 25(2), 133–140.

  3. Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24, 61–100.

  4. Brazeau-Lamontagne, L., Charlin, B., Gagnon, R., Samson, L., & Van Der Vleuten, C. (2004). Measurement of perception and interpretation skills during radiology training: Utility of the script concordance approach. Medical Teacher, 26(4), 326–332.

  5. Crowley, R. S., Naus, G. J., & Friedman, C. P. (2001). Development of visual diagnostic expertise in pathology. Proceedings of American Medical Informatics Association Symposium, 2001, 125–129.

  6. Crowley, R. S., Naus, G. J., Stewart, J., & Friedman, C. P. (2003). Development of visual diagnostic expertise in pathology: An information-processing study. Journal of the American Medical Informatics Association, 10, 39–51.

  7. 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.

  8. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2010). Attention guidance in learning from a complex animation: Seeing is understanding? Learning and Instruction, 20, 111–122.

  9. Elstein, A. S., Shulman, L. S., & Sprafka, S. A. (1978). Medical problem solving: An analysis of clinical reasoning. Cambridge, MA: Harvard University Press.

  10. Engel, P. J. H. (2008). Tacit knowledge and visual expertise in medical diagnostic reasoning: Implications for medical education. Medical Teacher, 30, 184–188.

  11. Eva, K. W. (2009). Diagnostic error in medical education: Where wrongs can make rights. Advances in Health Sciences Education, 14, 71–81.

  12. Gartmeier, M., Lehtinen, E., Gruber, H., & Heid, H. (2011). Negative expertise: Comparing differently tenured elder care nurses’ negative knowledge. European Journal of Psychology of Education, 26, 273–300. doi:10.1007/s10212-010-0042-5.

  13. Hamilton, P. W., van Diest, P. J., Williams, R., & Gallagher, A. G. (2009). Do we see what we think we see? The complexities of morphological assessment. Journal of Pathology, 218, 285–291.

  14. Helin, H., Lundin, M., Lundin, J., Martikainen, P., Tammela, T., van der Kwast, T., et al. (2005). Web-based virtual microscopy in teaching and standardizing Gleason grading. Human Pathology, 36, 281–286.

  15. Helle, L., Nivala, M., Kronqvist, P., Ericsson, K. A., & Lehtinen, E. (2010). Do prior knowledge, personality and visual perceptual ability predict student performance in microscopic pathology? Medical Education, 44, 621–629. doi:10.1111/j.1365-2923.2010.03625.x.

  16. Huk, T., Steinke, M., & Floto, C. (2009). The educational value of visual cues and 3D-representational format in a computer animation under restricted and realistic conditions. Instructional Science, 38(5), 455–469.

  17. Kohn, L. T., Corrigan, J. M., & Donaldson, M. S. (Eds.). (1999). To err is human: Building a safer health care system. Report of the Institute of Medicine. Washington, DC: National Academy Press.

  18. Kriz, S., & Hegarty, M. (2007). Top-down and bottom-up influences on learning from animations. International Journal of Human-Computer Studies, 65, 911–930.

  19. Krupinski, E. A., Tillack, A. A., Richter, L., Henderson, J. T., Bhattacharyya, A. K., Scott, K. M., Graham, A. R., Descrour, M. R., Davis, J. R., & Weinstein, R. S. (2006). Eye-movement study and human performance using telepathology virtual slides. Implications for medical education and differences with experience. Human Pathology, 37(12), 1543–1556. doi:10.1016/j.humpath.2006.08.024.

  20. Kundel, H. L., Nodine, C. F., & Carmody, D. M. A. (1978). Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Investigative Radiology, 13(3), 175–181.

  21. Kushniruk, A. W., Kaufman, R. D., Patel, V. L., Lévesque, Y., & Lottin, P. (1996). Assessment of a computerized patient record system: A cognitive approach to evaluation of an emerging medical technology. M.D. Computing, Computers in Medical Practice, 13, 406–415.

  22. Lin, L., & Atkinson, R. K. (2011). Using animations and visual cueing to support learning of scientific concepts and processes. Computers & Education, 56, 650–658.

  23. Myles-Worsley, M., Johnston, W. A., & Simons, M. A. (1988). The influence of expertise on X-ray image processing. Journal of Experimental Psychology. Learning, Memory, and Cognition, 14, 553–557.

  24. Patel, V. L., Arocha, J. F., & Zhang, J. (2005). Thinking and reasoning in medicine. In K. Holyoak (Ed.), Cambridge handbook of thinking and reasoning. Cambridge: Cambridge University Press.

  25. Patel, V. L., Groen, G. J., & Arocha, J. F. (1990). Medical expertise as a function of task difficulty. Memory & Cognition, 18(4), 394–406.

  26. Patel, V. L., & Kaufman, D. R. (2001). Medical expertise, cognitive psychology of International encyclopedia of the social & behavioral sciences. Oxford: Elsevier.

  27. Patel, V. L., Kaufman, D. R., & Arocha, J. F. (2002). Emerging paradigms of cognition in medical decision-making. Journal of Biomedical Informatics, 35(1), 52–75.

  28. Renkl, A., Atkinson, R. K., & Maier, U. H. (2000). From studying examples to solving problems: Fading worked-out solution steps helps learning. Proceedings of the 22nd annual conference of the cognitive science society.

  29. Wouters, P., Paas, F. G. W. C., & van Merriënboer, J. J. G. (2008). How to optimize learning from animated models: A review of guidelines based on cognitive load. Review of Educational Research, 78(3), 645–675.

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The research reported here has been funded by the Academy of Finland (FiDiPro project 8118371 and LearnMedImage project 8128766). A special thanks to WebMicroscope administrators MD, PhD, docent Johan Lundin and MD Mikael Lundin, Biomedical Informatics Research Group, HUCH Clinical Research Institute, Helsinki, Finland.

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Correspondence to Markus Nivala.

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Nivala, M., Säljö, R., Rystedt, H. et al. Using virtual microscopy to scaffold learning of pathology: a naturalistic experiment on the role of visual and conceptual cues. Instr Sci 40, 799–811 (2012). https://doi.org/10.1007/s11251-012-9215-8

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  • Medical education
  • Learning technology
  • Virtual microscopy
  • Visual cueing
  • Conceptual cueing