Open educational resources in distributed learning infrastructures: An international comparative study
Bibliographic record
Abstract
Open Educational Resources (OER) have the potential to support increased higher education access at a lower cost to rural, remote, lower-socio economic students, alongside lifelong learners and time-poor workers who require upskilling (Bossu & Meier, 2018; Orr, Rimini, & van Damme, 2015). The German Ministry of Education and Research (BMBF) has funded the interdisciplinary project ‘Digital educational architectures: Open learning resources in distributed learning infrastructures – EduArc’ (Learning Lab, 2019), a partnership between the University of Duisburg-Essen, the German Institute for International Educational Research, the Leibnitz Information Centre for Economics and the Carl von Ossietzky University of Oldenburg, in order to explore the development of disseminated learning infrastructures and enable national access to digital education resources. In order to produce infrastructure that is aligned with international developments and trends in higher education digital transformation, nine comparative country studies have been commissioned, alongside Germany, to be undertaken by members of the Centre for Open Education Research (www.uol.de/coer), namely Spain, China, Japan, Korea, Canada, South Africa, Turkey, USA and Australia. The country studies focus on digital transformation across the macro, meso and micro levels, and focus in particular on the infrastructure for disseminating OER in higher education, including repositories and meta-data standards. The studies also focus on national, state and institutional policies; quality assurance mechanisms and key actors; and how change (in terms of funding, managing and promoting infrastructure) is promoted and occurs at all three levels. To date, the macro studies have been completed. In terms of Infrastructure, Spain, China, Japan and Korea have national repositories, including OER, although they are not commonly used in Japanese universities. Canada and USA have decentralised infrastructure, with companies retaining intellectual property rights. All governments have recommendations and funding for digital transformation, however the majority of action is left to individual states and institutions. Spain, Korea and China have national standards for digitalisation, labelling, quality and/or meta-data standards, with the onus on institutions in other countries. The next stage of this research, the meso level, is currently underway.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".