B. Thoma is associate professor, Department of Emergency Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada, and clinician educator, Royal College of Physicians and Surgeons of Canada, Ottawa, Ontario, Canada; ORCID: https://orcid.org/0000-0003-1124-5786 .
R.H. Ellaway is professor, Department of Community Health Sciences, and director, Office of Health and Medical Education Scholarship, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; ORCID: https://orcid.org/0000-0002-3759-6624 .
Acad Med. 2021 Jul 1;96(7S):S89-S95. doi: 10.1097/ACM.0000000000004092.
The transition to the assessment of entrustable professional activities as part of competency-based medical education (CBME) has substantially increased the number of assessments completed on each trainee. Many CBME programs are having difficulty synthesizing the increased amount of assessment data. Learning analytics are a way of addressing this by systematically drawing inferences from large datasets to support trainee learning, faculty development, and program evaluation. Early work in this field has tended to emphasize the significant potential of analytics in medical education. However, concerns have been raised regarding data security, data ownership, validity, and other issues that could transform these dreams into nightmares. In this paper, the authors explore these contrasting perspectives by alternately describing utopian and dystopian futures for learning analytics within CBME. Seeing learning analytics as an important way to maximize the value of CBME assessment data for organizational development, they argue that their implementation should continue within the guidance of an ethical framework.
向基于能力的医学教育（CBME）中可委托专业活动评估的转变，大大增加了每个学员完成的评估数量。许多 CBME 项目在综合大量评估数据方面遇到了困难。学习分析是一种通过系统地从大数据集中得出推论来支持学员学习、教师发展和项目评估的方法。该领域的早期工作往往强调分析在医学教育中的巨大潜力。然而，人们对数据安全、数据所有权、有效性和其他可能将这些梦想变为噩梦的问题表示担忧。在本文中，作者通过交替描述 CBME 中学习分析的乌托邦和反乌托邦未来，探讨了这些相互矛盾的观点。作者将学习分析视为最大化 CBME 评估数据在组织发展方面价值的重要途径，他们认为，在伦理框架的指导下，应该继续实施其应用。
