The recognition of non-formal education in higher education: Where are we now, and are we learning from experience?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 it