The recognition of non-formal education in higher education: Where are we now, and are we learning from experience?
Bibliographic record
Abstract
The increasing availability of non-formal education in the form of Open Education Resources (OERs) and Massive Open Online Courses (MOOCs) gives rise to the questions of how such education can be formally recognized for credit. Prior Learning Assessment and Recognition (PLAR), and Qualification Frameworks are fields of practice actively engaged in and associated with the recognition of non-formal education (RNFE) and can provide guidance on RNFE for the recognition of OERs/MOOCs. A scoping exercise reviews the literatures from the three fields and associated practical exemplars. Findings suggest a growing demand for, growth in, and diversification of, the recognition of non-formal education. Synergies or creative combinations of expertise across the three fields that could be further exploited to gain maximum traction for RNFE are identified. These are multi-dimensional: top-down, bottom-up, sector to sector, country to country, qualification framework to qualification framework, system to system, field to field. There is ample evidence that the process of recognition, albeit demanding, does have a positive effect on the quality of the NFE, and by association, it is hoped, on the qualification status of individuals and their access to related social and economic benefits.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".