A Scoping Umbrella Review of Competency-Based Education: Part I – A Descriptive Analysis of Trends, Practices, and Gaps
Bibliographic record
Abstract
Competency-based education (CBE) has been increasingly adopted and popularized across diverse disciplines and professional fields. However, its implementation faces challenges due to the absence of a unified framework. Part I of this study aims to explore different theoretical approaches to CBE and to identify key approaches, emerging trends, and focal areas across diverse educational contexts, fields, and regions. A scoping umbrella review was conducted to synthesize evidence on CBE from systematic reviews, meta-analyses, and other reviews. Searches were performed across five databases by an experienced librarian, focusing on CBE frameworks and outcomes. Only peer-reviewed articles published in English were included. Four reviewers screened articles using Covidence software, resolving disagreements through discussion and collaboration. The umbrella review of 36 articles reveals key trends: (1) most are literature or systematic reviews, (2) published between 2017 and 2022, (3) primarily from the U.S. and Canada, and (4) focused on higher education and medical/health sciences. The review identifies three categories of theoretical approaches: theories, models, and frameworks. This review highlights the evolving theoretical foundations of CBE and its expansion across disciplines. The integration of various theories, models, and frameworks into a cohesive meta-framework emerges as a critical next step.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.101 | 0.085 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".