OER Awareness and Use: The Affinity Between Higher Education and K-12
Bibliographic record
Abstract
Educators within Higher Education (HE) and K-12 share in the need for high quality educational resources to assist in the pursuit of teaching and learning. Although there are numerous differences between the two levels of education, there are commonalties in the perceptions of the purpose, practical uses, and challenges that abide in the use of Open Educational Resources (OER). Observations made while producing podcasts and videos for OER awareness, use, and championing, form an exposition of the merits of OER for HE and K-12. Benefits include cost-savings in acquiring resources for teaching and learning as well as user-generated content, instructor creativity, and contextualized and responsively timely learning opportunities. Additionally, the teaching culture of K-12 has historically supported the sharing of learning activities and learning resources. At all levels of education, OER awareness requires a deeper understanding of the changes to teaching and learning borne by open educational practices.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".