Accelerating expertise: Perceptual and adaptive learning technology in medical learning

Abstract

Rationale: Recent advances in the learning sciences offer remarkable potential for improving medical learning and performance. Difficult to teach pattern recognition skills can be systematically accelerated using techniques of perceptual learning (PL). The effectiveness of PL interventions is amplified when they are combined with adaptive learning (AL) technology in perceptual–adaptive learning modules (PALMs).

Innovation: Specifically, PALMs incorporate the Adaptive Response Time-based Sequencing (ARTS) system, which leverages learner performance (accuracy and speed) in interactive learning episodes to guide the course of factual, perceptual, or procedural learning, optimize spacing, and lead learners to comprehensive mastery. Here we describe elements and scientific foundations of PL and its embodiment in learning technology. We also consider evidence that AL systems utilizing both accuracy and speed enhance learning efficiency and provide a unified account and potential optimization of spacing effects in learning, as well as supporting accuracy, transfer, and fluency as goals of learning.

Results: To illustrate this process, we review some results of earlier PALMs and present new data from a PALM designed to accelerate and improve diagnosis in electrocardiography.

Conclusions: Through relatively short training interventions, PALMs produce large and durable improvements in trainees’ abilities to accurately and fluently interpret clinical signs and tests, helping to bridge the gap between novice and expert clinicians.

Acknowledgments

We gratefully acknowledge support from the agencies listed in the Funding section below. Any opinions, findings, and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of NSF, the U.S. Department of Education, NIH or other agencies. We thank Rachel Older for general assistance and Christine Massey for helpful discussions. A number of features of the perceptual and adaptive learning technologies described here are covered by U.S. Patents 7052277 and 9299265. For more information, please contact [email protected]

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Additional information

Funding

This work was supported by the US National Science Foundation [grant number DRL 1644916]; the U.S. Department of Education, Institute of Education Sciences (IES), Cognition and Student Learning (CASL) Program [grant number R305A120288]; the US National Institute of Health [grant number 5RC1HD063338], and a grant from iInTIME/UMed.

Notes on contributors

Philip J. Kellman

Philip J. Kellman, PhD, is Distinguished Professor of Psychology at UCLA and Adjunct Professor in the Department of Surgery at the David Geffen School of Medicine, UCLA.

Sally Krasne

Sally Krasne, PhD, is Research Professor of Physiology at the David Geffen School of Medicine, UCLA and a member of UCLA’s Brain Research Institute.