From Utopia Through Dystopia

Charting a Course for Learning Analytics in Competency-Based Medical Education

Dados Bibliográficos

ID21614780
AutoresBerenike Thomas (0000-0003-1124-5786, 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:., autor correspondente), Rachel Ellaway (0000-0002-3759-6624, 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:.), Teresa M Chan (0000-0001-6104-462X, is associate professor, Division of Emergency Medicine, Department of Medicine, assistant dean, Program for Faculty Development, Faculty of Health Sciences, and adjunct scientist, McMaster Education Research, Innovation, and Theory (MERIT) program, McMaster University, Hamilton, Ontario, Canada; ORCID:.)
Ano2021
Volume96
Fascículo7S
PáginasS89-S95
Data de publicação2021-07-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoAcademic Medicine (JOURNAL)
Identificadores do periódicoISSN: 1040-2446 • E-ISSN: 1938-808X
EditoraOxford University Press (OUP) (PUBLISHER)
DOI10.1097/acm.0000000000004092
PMID34183609
OpenAlexW3175170542
IdiomaEN
Citações recebidas7
Referências citadas45

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

Analytics · Data Analysis · Data science · Dystopia · Learning analytics · Medical education · Artificial Intelligence in Healthcare and Education · Computer Science · Innovations in Medical Education · Medicine · Psychology · Radiology practices and education · Artificial Intelligence