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.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".