Report on the Assessment and Accreditation of Learners using Open Education Resources (OER)
Bibliographic record
Abstract
This report shares the findings and lessons learned from an investigation into the economics of disaggregated models for assessing and accrediting informal learners, with particular attention to the OER University (OERu) consortium. It also relies on data from a small-scale survey conducted by two of the authors on perceptions, practices and policies relating to openness in assessment and accreditation in post-secondary institutions (Murphy & Witthaus, 2012). These investigations include the perceptions of stakeholders in post-secondary education towards the OERu concept, combined with a look at economic models for universities to consider in implementing OER assessment and accreditation policies. ... This report was prepared under the UNESCO/COL Chair in OER programme, with funding from the Social Sciences and Humanities Research Council (SSHRC) of Canada, the Commonwealth of Learning and the Technology Enhanced Knowledge Research Institute (TEKRI) of Athabasca University. (DIPF/Orig.)
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".