Bibliographic record
Abstract
The concept of open educational resources (OER) is becoming increasingly prominent in education. However, research circles around defining OER, content and forms of OER, technological features of OER, and the importance of the issue or lack thereof. Vital aspects such as the notion of the adoption of OER by educational practitioners remain underdeveloped. In order to shed light on the question of how to adopt OER in education, the article presents findings of a meta-study which critically reviewed 25 state-funded OER projects located in Germany. All projects aimed to anchor OER across educational areas, such as school, higher, continuing, and vocational education. The meta-analysis disclosed a mixed bag of results. Although interest and willingness to deal with OER can be confirmed, reservation is rooted in the complexity of the topic and especially the legal concerns. However, the findings demonstrate that OER can by no means be ignored in the context of teaching and learning in a digital world. Integrating OER as an aspect of existing educational training should, therefore, be encouraged. Concerning future design recommendations, to conflate OER with other pressing issues and to simultaneously emphasise its added value explicitly is a promising approach. Moreover, establishing central contact points in educational institutions to accompany and monitor actors on their path to OER appears to be necessary. Notwithstanding the concrete measures, any strategy must operate persistently at both levels, institutional and practical, embracing all relevant stakeholders.
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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 | 0.068 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".