a Department of Psychology , University of California , Los Angeles , CA , USA.
b Department of Physiology , David Geffen School of Medicine, University of California , Los Angeles , CA , USA.
Med Teach. 2018 Aug;40(8):797-802. doi: 10.1080/0142159X.2018.1484897. Epub 2018 Aug 9.
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).
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.
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.
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.
最近学习科学的进展为改善医学学习和表现提供了巨大的潜力。使用感知学习 (PL) 技术可以系统地加速教授难以教授的模式识别技能。当将感知自适应学习模块 (PALM) 中的 PL 干预措施与自适应学习 (AL) 技术结合使用时，其效果会放大。
具体来说，PALM 采用了自适应响应时间排序 (ARTS) 系统，该系统利用学习者在互动学习中的表现（准确性和速度）来指导事实、感知或程序学习的过程，优化间隔，并引导学习者全面掌握知识。在这里，我们描述了 PL 的要素和科学基础及其在学习技术中的体现。我们还考虑了利用准确性和速度来提高学习效率的 AL 系统的证据，并提供了对学习中间隔效应的统一解释和潜在优化，以及对准确性、迁移和流畅性作为学习目标的支持。
为了说明这一过程，我们回顾了早期 PALM 的一些结果，并展示了一个旨在加速和改善心电图诊断的 PALM 的新数据。
通过相对较短的培训干预，PALM 可大大提高学员准确流畅地解读临床体征和测试的能力，有助于缩小新手和专家临床医生之间的差距。
