From Utopia Through Dystopia: Charting a Course for Learning Analytics in Competency-Based Medical Education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".