A Systematic Review of Systematic Reviews on Open Educational Resources: An Analysis of the Legal and Technical Openness
Bibliographic record
Abstract
Almost all open educational resources (OER) definitions encompass key concepts such as the 5R activities and open licenses. However, little attention is given to the technical aspects and tools that allow the user to interact with these resources. This study aims to answer five research questions regarding (a) 5R activities, (b) open licenses and intellectual property, (c) technical aspects, (d) tools for developing OER, and (e) the topic of sustainability. To answer these questions, a systematic review of systematic reviews on OER was conducted following the reporting checklist of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Sixteen studies were eligible and included in this review. The main findings suggest that although most studies did not mention the term 5R exactly, they mentioned related terms, such as share and adaptation. There was also a tendency toward focusing on more legal issues than technical aspects. Besides, most of the studies that mentioned tools discussed them as platforms to access OER, not exactly tools that encourage users to develop or adapt resources in an easy way. In relation to sustainability, several studies highlighted the relevance of developing sustainable OER models, but only a few suggested approaches to sustain an OER project. Therefore, with this article, we hope to raise awareness of the importance of the technical openness and tools that might contribute to fostering users’ engagement with the OER, helping them to act as producers and contributors rather than mere passive receivers.
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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 | 0.070 | 0.262 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.033 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".