Quality Assurance for Open Educational Resources: The OERTrust Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it