Applying perceptual and adaptive learning techniques for teaching introductory histopathology

Abstract

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 1st-year students, but not significantly so for 2nd-year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1st and 2nd 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.

Adaptive response-time-based category sequencing in perceptual learning

“…This is true even in high-level, symbolic domains, such as mathematics, chess, and reading (de Groot, 1965; Chase & Simon, 1973; Goldstone, Landy, & Son, 2008; Kellman, Massey, & Son, 2010; Kellman & Massey, 2013; Thai, Mettler, & Kellman, 2011), where PL functions synergistically with other aspects of cognition. Learning technology based on PL, in the form of perceptual learning modules (PLMs), has been shown to accelerate crucial and otherwise elusive aspects of learning, including pattern recognition, transfer, and fluency, in domains as diverse as aviation training (Kellman & Kaiser, 1994), mathematics learning (Kellman, Massey, & Son, 2010; Massey, Kellman, Roth, & Burke, 2010), and medical learning (Drake, Krasne, Kellman, & Hillman, in press; Guerlain et al, 2004; Kellman, 2013). …”

A comparison of adaptive and fixed schedules of practice.

“…The techniques developed here have already been applied to real world learning problems such as mathematics learning (Mettler, Massey & Kellman, 2011) and extend to perceptual or category learning (Mettler & Kellman, 2014), such as the training of expertise in perceptual learning in domains like aviation and a number of medical learning domains, such as echocardiography, radiology, dermatology and pathology (Krasne, Hillman, Kellman, & Drake, 2013; Krasne, Rimoin, Altieri, Craft & Kellman, 2015; Thai, Krasne & Kellman, 2015). It is important to note that there will likely be some differences when laboratory studies such as those in this paper are generalized to large scale, real-world educational domains.…”

“…However, the techniques described in this paper have already been successfully deployed in large scale studies, with longterm consequences for learning. In work applying the adaptive learning system described here to perceptual category learning in medical domains, for example, learning gains in a Histopathology perceptual adaptive learning module (PALM) were substantially preserved in delayed posttests given 6–7 weeks later (Krasne et al, 2013); in a Dermatology PALM, advantages for students who completed the module over those who did not were clearly evident in delayed tests given a year later (Krasne et al, 2015), and in an Echocardiography PALM, 3rd-year medical students who invested about 45 minutes per day for two days to complete the module outperformed second year emergency medicine residents, for whom ECG interpretation is a centrally important skill, with the learning gains for the PALM group being substantially preserved in delayed posttest given a year later (Neiman, Stevens, Kellman & Krasne, submitted). Adaptive systems based on ongoing assessment of learning strength can likely enhance learning in any domain where spacing and scheduling are important moderators of long-term learning strength.…”

NDER: A novel web application using annotated whole slide images for rapid improvements in human pattern recognition

“…[ 7 ] These techniques take advantage of preattentive processing, the phenomenon of forming a quick (<200 ms) impression of an image. [ 7 ] Herein, the term RPR is used to refer to this phenomenon, although many terms are used across multiple disciplines in the literature. [ 5 7 8 9 ]…”