Editorial – The Landscape of Prior Learning Assessment: A Sampling from a Diverse Field
Bibliographic record
Abstract
We live today, as educators and global citizens, in a time of convergence, due in great part to the phenomenal rise of social media and networking tools that have reduced barriers to and boundaries between exchange and domains, beyond what our early trail-blazing distance educators could even have imagined. This special issue of IRRDOL features another exciting convergence – one that represents the somewhat narrow overlap of interests between open and distance learning (ODL) and the recognition of prior learning (RPL). In this special issue, therefore, we offer a selection of articles and practitioner-based reports that, in varying measures, span both fields. Two important foundational issues underlying the prior learning field should be addressed here. The first concerns language. Prior learning recognition, although practiced globally, has a wide variety of labels and acronyms. In Canada, we are most likely to refer to it as PLAR – prior learning assessment and recognition. RPL – recognition of prior learning – is also beginning to become popular in certain Canadian jurisdictions. In the United States, PLA – prior learning assessment – is the most common label. Around the rest of the world, the shared understanding that informally or experientially attained learning can be recognized for credit toward credentialization is referred to as the assessment of prior learning (APL), the assessment/accreditation of prior and experiential learning (APEL), and a variety of other similar terms. You will notice, in the articles in this issue, a variety in terminology that would logically accompany an international publication. This short editorial uses the PLAR acronym. International
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".