An International Study in Competency Education: Postcards from Abroad
Bibliographic record
Abstract
Acknowledging that national borders need not constrain our thinking, we have examined a selection of alternative academic cultures and, in some cases, specific schools, in search of solutions to common challenges we face when we consider reorganizing American schools. A wide range of interviews and e-mail exchanges with international researchers, government officials and school principals has informed this research, which was supplemented with a literature review scanning international reports and journal articles. Providing a comprehensive global inventory of competency-based education is not within the scope of this study, but we are confident that this is a representative sampling. The report that follows first reviews the definition of competency-based learning. A brief lesson in the international vocabulary of competency education is followed by a review of global trends that complement our own efforts to improve performance and increase equitable outcomes. Next, we share an overview of competency education against a backdrop of global education trends (as seen in the international PISA exams), before embarking on an abbreviated world tour. We pause in Finland, British Columbia (Canada), New Zealand and Scotland, with interludes in Sweden, England, Singapore and Shanghai, all of which have embraced practices that can inform the further development of competency education in the United States.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".