From OER to PLAR: Credentialing for open education
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
Recent developments in OER and MOOCs (Open Educational Resources and Massive Open Online Courses) have raised questions as to how learners engaging with these courses and components might be assessed or credentialed. This descriptive and exploratory paper examines PLAR (Prior Learning Assessment and Recognition) as a possible answer to these questions. It highlights three possible connections between PLAR and open education which hold the greatest promise for credentialing open learning experiences: 1) PLAR may be used to assess and credential open educational activities through the use of exam banks such as CLEP (College Level Examination Program); 2) Learning occurring in xMOOCs (MOOCs based on already credentialed courses) and in other open contexts resembling “courses” may be assessed in PLAR through course-based portfolios; and 3) PLAR may also be enabled through the specification of “gap learning” facilitated through OER of many different kinds. After describing these options, the paper concludes that although the connections leading from open educational contexts to PLAR credentialing are currently disparate and <em>ad hoc</em>, they may become more widespread and also more readily recognized in the PLAR and OER communities.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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