Open Educational Resource Policy Development at a Campus of the University of the West Indies
Bibliographic record
Abstract
Open educational resources (OER) are critical tools recognized by UNESCO and the Commonwealth of Learning (COL) for achieving the United Nations’ Sustainable Development Goals (SDGs), specifically SDG 4: Quality education. The University of the West Indies, St. Augustine Campus (UWISTA), undertook an initiative to formulate an OER policy, aiming to align with the UWI’s mission and general OER principles of openness, accessibility, affordability, and innovation. This paper outlines the comprehensive approach adopted, including online research, document review, surveys, focus groups, and a three-day workshop, ensuring diverse staff perspectives. The policy development process commenced with a thorough review of 44 existing OER policies, facilitated by consulting relevant documents and scholarly resources and an online survey. Subsequent stages included two Delphi focus groups and an on-site workshop in which participants actively contributed to drafting a policy. The draft OER policy that emerged from this process reflects a consensus among participants and incorporates best practices gleaned from the examination of other institutional policies. Key observations from this initiative emphasize the importance of a collaborative approach, the use of existing models, transparency in policy development, continuous support, and addressing copyright issues. Generative artificial intelligence was actively employed by the workshop participants, especially for comparing policy and process items under consideration. UWISTA’s OER policy development, supported by the COL, serves as a model for other institutions aiming to embrace open education principles. The draft policy, emerging from this inclusive and transparent process, aligns with UWI’s mission and broader OER goals, offering valuable insights for the academic community and policymakers globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".