Not All Rubrics Are Equal: A Review of Rubrics for Evaluating the Quality of Open Educational Resources
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
<p>The rapid growth in Internet technologies has led to a proliferation in the number of Open Educational Resources (OER), making the evaluation of OER quality a pressing need. In response, a number of rubrics have been developed to help guide the evaluation of OER quality; these, however, have had little accompanying evaluation of their utility or usability. This article presents a systematic review of 14 existing quality rubrics developed for OER evaluation. These quality rubrics are described and compared in terms of content, development processes, and application contexts, as well as, the kind of support they provide for users. Results from this research reveal a great diversity between these rubrics, providing users with a wide variety of options. Moreover, the widespread lack of rating scales, scoring guides, empirical testing, and iterative revisions for many of these rubrics raises reliability and validity concerns. Finally, rubrics implement varying amounts of user support, affecting their overall usability and educational utility.</p>
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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 | 0.065 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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