Perceptual learning modules in undergraduate dermatology teaching

Abstract

Background: Dermatological diagnosis depends highly on visual skills, and implicit nonanalytical proficiency plays a key role. To correctly diagnose skin diseases, the clinician needs visual skills, and intuitive recognition plays a key role.

Aim: To investigate the effectiveness of digital perceptual learning modules (PLMs) in undergraduate teaching, and how these affect medical students' learning about skin diseases.

Methods: This was a study performed in Finland, which enrolled 39 students of an undergraduate dermatology course. Online PLMs designed for dermatology, using different pictures of skin diseases were performed three times: before, during and at the end of the course. The modules provided four outcome measures: diagnostic accuracy (percentage of correct responses), a rating of confidence about the decision, fluency (response/decision time) and a list of features on which the decision was based.

Results: As the number of PLMs and the course duration increased, there were also improvements in the four measures, with a significant increase in diagnostic accuracy [from 66% to 94%; P < 0.001; partial η² (η² _p ) = 0.92], fluency (as measured by a decrease in response time (from 10 to 6 s; P < 0 0.001; η² _p = 0.69) and self-perceived confidence (2.5 to 4.3; P < 0 0.001, η² _p = 0.86) with subsequent PLMs and course duration. There was a diversification of recognized features, an increase in pattern recognition, and better attention to localization and contextual association. Based on student feedback, the PLMs functioned well online, and enhanced motivation and learning.

Conclusion: PLMs increased diagnostic accuracy, had a positive effect on learning outcomes and were easily integrated alongside clinical teaching. Considering the current era of digital technologies, we believe that there is potential for wider use of PLMs to improve visual skills and strengthen implicit learning in dermatology.

Figure 1

Correct responses (a) and confidence ratings (b) in two clustered pre‐, mid‐ and post‐tests. Repeat A indicates the first test, repeat B the second test.

Figure 2

Decision times in two pre‐, mid‐ and post‐tests (repeated tests A and B).

Figure 3

Percentages of selected features in two pre‐, mid‐ and post‐tests (repeated tests A and B). Feature groups: primary lesions, secondary lesions, pattern recognition, location of the lesions, context of the picture, other important features.