Not All Rubrics Are Equal: A Review of Rubrics for Evaluating the Quality of Open Educational Resources
Bibliographic record
Abstract
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.
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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 | 0.079 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".