Flexible paths to assessment for OER learners: A comparative study
Bibliographic record
Abstract
This paper highlights the preliminary findings of a one-year research project (2011) that investigated the fit of recognizing prior learning (RPL) practice and related assessment and transfer protocols to projected OER use, especially by the Open Educational Resource University (OERu), a newly-formed consortium of like-minded institutions located worldwide. Across a study that included 31 post secondary institutions from 10 countries, findings indicated both consistencies and inconsistencies in the treatment of RPL. While most institutions reflected the intent of honoring learners' prior learning, achieved informally or non-formally, institutions were bound by internal policy and structure in terms of protocols. The relationship of transfer credit opportunities to engaging with learners in preparing RPL documents for assessment was also varied. Broad disparities in fee information made it difficult to determine what the actual costs of various protocols would be for learners. OERu will continue to search for innovative approaches to providing universal and collaborative education, globally, to non-traditional learners. <span class="sub_head">Keywords:</span> Open Educational Resources, OER, Open Educational Resource University, OERu, assessment, recognition of prior learning, RPL, access, credentialisation, policy
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".