1. Remote histology learning from static versus dynamic microscopic images.
- Author
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Mione S, Valcke M, and Cornelissen M
- Subjects
- Adolescent, Female, Humans, Male, Retrospective Studies, Histology education, Learning, Microscopy
- Abstract
Histology is the study of microscopic structures in normal tissue sections. Curriculum redesign in medicine has led to a decrease in the use of optical microscopes during practical classes. Other imaging solutions have been implemented to facilitate remote learning. With advancements in imaging technologies, learning material can now be digitized. Digitized microscopy images can be presented in either a static or dynamic format. This study of remote histology education identifies whether dynamic pictures are superior to static images for the acquisition of histological knowledge. Test results of two cohorts of second-year Bachelor in Medicine students at Ghent University were analyzed in two consecutive academic years: Cohort 1 (n = 190) and Cohort 2 (n = 174). Students in Cohort 1 worked with static images whereas students in Cohort 2 were presented with dynamic images. ANCOVA was applied to study differences in microscopy performance scores between the two cohorts, taking into account any possible initial differences in prior knowledge. The results show that practical histology scores are significantly higher with dynamic images as compared to static images (F (1,361) = 15.14, P < 0.01), regardless of student's gender and performance level. Several reasons for this finding can be explained in accordance with cognitivist learning theory. Since the findings suggest that knowledge construction with dynamic pictures is stronger as compared to static images, dynamic images should be introduced in a remote setting for microscopy education. Further implementation within a larger electronic learning management system needs to be explored in future research. Anat Sci Educ 9: 222-230. © 2015 American Association of Anatomists., (© 2015 American Association of Anatomists.)
- Published
- 2016
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