Kellman Philip J
Department of Psychology, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1563.
Mil Med. 2013 Oct;178(10 Suppl):98-106. doi: 10.7205/MILMED-D-13-00218.
Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains.
学习科学的最新进展为改善医学教育和最大化新兴医学技术的益处提供了巨大潜力。本文介绍了学习科学中适用于模拟及医学学习其他方面的两个主要创新领域:感知学习(PL)和自适应学习技术。PL技术首次提供了基于计算机的系统方法,用于教授模式识别、结构直觉、迁移和流畅性。与PL协同的是新的自适应学习技术,这些技术可为每个个体优化学习、嵌入客观评估并实施掌握标准。作者介绍了基于自适应响应时间的排序(ARTS)系统,该系统利用每个学习者在交互式学习中的准确性和速度来指导间隔、排序和掌握程度。在最近的研究中,这些新技术已应用于医学学习环境,包括用于初始医学诊断的自适应学习模块以及皮肤科、组织学和放射学中的感知/自适应学习模块(PALM)。所有这些研究结果表明,感知学习和自适应学习技术单独或组合使用,在改善各种医学领域的学习方面具有巨大潜力。