Institutional Measures for Supporting OER in Higher Education: An International Case-Based Study
Bibliographic record
Abstract
Abstract Open Educational Resources (OER) in higher education cannot be put into practice without considering institutional contexts, which differ not only globally but also within the same country. Each institutional context provides educators with opportunities or limitations where Open Educational Practices (OEP) and OER for teaching and learning are involved. As part of a broader research project, and as a follow-up to national perspectives, an international comparison was conducted, based on institutional cases of nine different higher education systems (Australia, Canada, China, Germany, Japan, South Africa, South Korea, Spain, Turkey). Aspects regarding the availability of infrastructure and institutional policies for OER, as well as the existence of measures directed at OER quality assurance and at the promotion of the development and use of OER were covered. The resulting theoretical contribution sheds light on an international comparative view of OER and points towards country-specific trends, as well as differences among institutions. These aspects could provide an impetus for the development of institutional guidelines and measures. In line with international literature on the topic, recommendations are derived to promote/ enhance the use of OER in teaching and learning in higher education at the institutional level.
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.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".