Quality Assurance for Open Educational Resources: The OERTrust Framework
Bibliographic record
Abstract
Learning Objects have met some barriers to their development and effective adoption, which varied from the lack of quality assurance mechanisms to the impossibility of editing and adapting most of them to real teaching and learning contexts. However, with the advent of OER (Open Educational Resources), if the later problem – to retain, reuse and even remix learning content – was meant to be solved, the same could not be said for the first one. Quality assurance is still an unsolved problem in this context, even more complex due to the possibility of versioning and collaborative design brought by OER. Thus, it is necessary to propose validation mechanisms for them, at least establishing some guarantees about their functionality and quality. In this sense, this work aims to discuss OERTrust, a proposal of supporting framework for OER validation and testing process, considering both versioning and remixing features. OERTrust is based on the principles of validation and testing that come from Software Engineering area and relies on fuzzy logic to define the importance and influence of different tests to each kind of OER. https://doi.org/10.26803/ijlter.17.3.1
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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 | 0.073 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".