Partnering Higher Education and K–12 Institutions in OER: Foundations in Supporting Teacher OER-Enabled Pedagogy
Bibliographic record
Abstract
Open educational resources (OER) are disproportionately created and/or accessed by institutions of higher education as compared to K–12 even though teachers confront the challenge of outdated teaching materials or, worse, an increasing trend by school districts to discontinue textbook adoption altogether. In this paper, we describe a sustainable and innovative example of OER-enabled pedagogy (OEP) that partners teachers and students across institutional boundaries to address these problems. The Pathways Project (PP) is a higher education and K–12 community of 350 world-language teachers, students, and staff that engage in the 5Rs (retain, reuse, revise, remix, and redistribute) of OEP with a repository of more than 800 OER ancillary activities that support standards-based pedagogy for 10 world languages and cultures. The PP is innovative because it fosters renewable assignments for the entire disciplinary ecosystem unlike most OEP studies that discuss renewable assignments limited to a single course. Teacher education is one of the best places to engage OEP because teachers are trained to personalize and contextualize OER materials for their local classroom needs. In so doing, the PP community receives timely discipline-specific professional development that is in high demand, especially in rural communities where teachers are isolated. Higher education-K–12 OEP partnerships are rare, and yet teacher education programs exist in most universities and can be a logical place to start. This paper provides concrete examples and practical steps that are transferable to other disciplines looking to engage in similar types of OER-OEP collaboration and community engagement.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".