Open Education and Alternative Digital Credentials in Europe
Bibliographic record
Abstract
Learners who learn from OER often cannot have their learning assessed or receive a credential. Open credentials offer a potential solution to this problem, combining badges or micro-credentials with competence frameworks and digital seals. This study identified the current situation of open credentials in post-secondary education in Europe, the main themes of the discourse, and the points of agreement and divergence surrounding them. The data comprised a corpus of transcriptions from 12 expert interviews and a focus group. Qualitative text analysis identified the principal themes. Findings included the following: (a) few assessments are available as open content; (b) linking OER and credentials requires detailed and expensive work on learning outcomes and assessment; (c) the aggregation of open credentials to create higher-level qualifications is a widely accepted ambition; (d) the European Union’s infrastructure to support open credentials is appropriate and effective and can foster trust; (e) the outstanding challenges are organisational and practical, not technological; (f) assessment and content provisions should belong to separate organisational functions; and finally, (g) funding and support for open credentials in professional accreditation are essential for further progress.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".