How Results are Created: Methodological Approaches to the Design of Educational Outcomes
Bibliographic record
Abstract
The authors make retrospective analyze of "outcome-based education" and “competence-based education” concepts evolution. National educational standards and curricula all around the world have gone through dramatic transformation over the last 20 years. New education models require new methodology for designing educational outcomes. In the paper, based on the analysis of international experience, a framework for designing outcomes is proposed. Authors analyze Canada, UK, Finland, Australia, USA experience. Also terminology of meta-subject and subject outcomes are discusses in the paper. The framework presented by the authors is applicable both for school and teacher curricula, and at the national level, when formulating standards or example currucula.
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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 | 0.529 | 0.598 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".