An empirical framework for mainstreaming OER in an academic institution
Bibliographic record
Abstract
Purpose There is immense potential in open educational resources (OER) for encouraging systemic change within academic institutions toward increasing access and equity in education. The purpose of this paper is to propose an empirical framework and a checklist for mainstreaming OER in an academic institution. Design/methodology/approach The empirical framework and the mainstreaming checklist is formulated based on an extensive review of literature and case studies strengthened by the author’s personal experience as an academic, researcher, practitioner, policymaker and international development expert in the field of OER. Findings The proposed empirical framework and OER mainstreaming checklist identifies several processes to be completed by key stakeholders for successful mainstreaming of OER in an academic institution. Practical implications The proposed framework assumes that the institution which is undergoing mainstreaming of OER follows the principles of outcomes-based education and that it has an established mechanism for measuring the mastery of learning outcomes and the role of OER in accreditation. Originality/value One key feature of the framework is its horizontal structure where stakeholders take a team-based approach to completing the required tasks for mainstreaming OER. This, in turn, increases ownership of the mainstreaming process leading to higher success rates and sustainability. Second, the mainstreaming checklist breaks down each process into several achievable tasks and assigns them to the relevant team. Third, the framework supports continuous quality improvement which encourages institutions to periodically revisit the processes to make necessary course corrections and enhancements.
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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 | 0.089 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".