Journal of Pathology Informatics (Jan 2022)
Novel Diagnostic Educational Resource: Use of a web-based adaptive learning module to teach inflammatory reaction patterns in dermatopathology to medical students, residents, and fellows
Abstract
Background: Perceptual and adaptive learning modules (PALM’s) provide a large number of visual examples for evaluation and accommodate to learner performance by actively adjusting the module parameters. Methods: We developed a module for discriminating 5 inflammatory reaction patterns using the Novel Diagnostic Educational Resource (NDER) platform. The module included a 20 question pre-test, a 200 question training section, and a 20 question post-test. During the pre-test and post-test, images were displayed for an indefinite period of time with no feedback given. In the training section, images were displayed for a duration inverse to learner performance, and after submitting their response learners were immediately shown the correct answer. The performance of module participants was compared to a control group who completed pre-test and post-test only. Results: 26 pathology and dermatology residents completed the module and were included in analysis. Pre-test and post-test scores showed an average increase of 17.1 percentage points (95% CI 13.0 to 21.2, P < 0.001). When performance on pre-test and post-test was compared between the module and control groups, module group performance increased more than control group performance by an average of 10.1 percentage points (95% CI -2.5 to 17.8, P = 0.0119). 84% (37) of participants found the module somewhat useful or very useful and 68% (30) of participants would be pretty likely or very likely to recommend to another trainee. Conclusions: Our findings validate the use of NDER for teaching inflammatory reaction patterns. Participants generally had favorable feedback regarding the interface and teaching potential of the module. Including a late re-test as part of the module would be beneficial in further validating future iterations. Next steps include optimizing module performance and developing module content for more advanced learners.