Peer-To-Peer Recognition of Learning in Open Education
Bibliographic record
Abstract
Recognition in education is the acknowledgment of learning achievements. Accreditation is certification of such recognition by an institution, an organization, a government, a community, etc. There are a number of assessment methods by which learning can be evaluated (exam, practicum, etc.) for the purpose of recognition and accreditation, and there are a number of different purposes for the accreditation itself (i.e., job, social recognition, membership in a group, etc). As our world moves from an industrial to a knowledge society, new skills are needed. Social web technologies offer opportunities for learning, which build these skills and allow new ways to assess them. This paper makes the case for a peer-based method of assessment and recognition as a feasible option for accreditation purposes. The peer-based method would leverage online communities and tools, for example digital portfolios, digital trails, and aggregations of individual opinions and ratings into a reliable assessment of quality. Recognition by peers can have a similar function as formal accreditation, and pathways to turn peer recognition into formal credits are outlined. The authors conclude by presenting an open education assessment and accreditation scenario, which draws upon the attributes of open source software communities: trust, relevance, scalability, and transparency.
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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 | 0.030 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".