Applying perceptual and adaptive learning techniques for teaching introductory histopathology
Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive scie...
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Wolters Kluwer Medknow Publications
2013-01-01
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doaj-5e8dcf8a4fe849daa6318dd6912bc5322020-11-24T23:13:32ZengWolters Kluwer Medknow PublicationsJournal of Pathology Informatics2153-35392153-35392013-01-0141343410.4103/2153-3539.123991Applying perceptual and adaptive learning techniques for teaching introductory histopathologySally KrasneJoseph D HillmanPhilip J KellmanThomas A DrakeBackground: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses) that appear during a learning session based on each learner′s accuracy and response time (RT). We developed a perceptual and adaptive learning module (PALM) that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 st -year students, but not significantly so for 2 nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 st and 2 nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students.http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=1;spage=34;epage=34;aulast=KrasneCognitive sciencedermatologymedical educationpathologyperceptual learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sally Krasne Joseph D Hillman Philip J Kellman Thomas A Drake |
spellingShingle |
Sally Krasne Joseph D Hillman Philip J Kellman Thomas A Drake Applying perceptual and adaptive learning techniques for teaching introductory histopathology Journal of Pathology Informatics Cognitive science dermatology medical education pathology perceptual learning |
author_facet |
Sally Krasne Joseph D Hillman Philip J Kellman Thomas A Drake |
author_sort |
Sally Krasne |
title |
Applying perceptual and adaptive learning techniques for teaching introductory histopathology |
title_short |
Applying perceptual and adaptive learning techniques for teaching introductory histopathology |
title_full |
Applying perceptual and adaptive learning techniques for teaching introductory histopathology |
title_fullStr |
Applying perceptual and adaptive learning techniques for teaching introductory histopathology |
title_full_unstemmed |
Applying perceptual and adaptive learning techniques for teaching introductory histopathology |
title_sort |
applying perceptual and adaptive learning techniques for teaching introductory histopathology |
publisher |
Wolters Kluwer Medknow Publications |
series |
Journal of Pathology Informatics |
issn |
2153-3539 2153-3539 |
publishDate |
2013-01-01 |
description |
Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses) that appear during a learning session based on each learner′s accuracy and response time (RT). We developed a perceptual and adaptive learning module (PALM) that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a "Score" that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1 st -year students, but not significantly so for 2 nd -year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1 st and 2 nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students. |
topic |
Cognitive science dermatology medical education pathology perceptual learning |
url |
http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=1;spage=34;epage=34;aulast=Krasne |
work_keys_str_mv |
AT sallykrasne applyingperceptualandadaptivelearningtechniquesforteachingintroductoryhistopathology AT josephdhillman applyingperceptualandadaptivelearningtechniquesforteachingintroductoryhistopathology AT philipjkellman applyingperceptualandadaptivelearningtechniquesforteachingintroductoryhistopathology AT thomasadrake applyingperceptualandadaptivelearningtechniquesforteachingintroductoryhistopathology |
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