Opening up Educational Practices through Faculty, Librarian, and Student Collaboration in OER Creation: Moving from Labor-intensive to Supervisory Involvement
Bibliographic record
Abstract
This article presents a case study for transitioning library-led open-educational resources (OER) initiatives away from labor-intensive activities to a model where library personnel focus on project management responsibilities. This shift from labour-intensive activities, such as workshops and training sessions, led to more collaborative partnerships with faculty and students to produce OER projects. In particular, we focus on labour implications for the various stakeholders involved and the sustainability of these initiatives. We describe several initiatives undertaken by the Ohio University Libraries to encourage open educational resource adoptions and projects, including a grant-funded initiative to provide support services for faculty creating OER. That funding, which was awarded to enhance undergraduate education, has been used to support the development of five OER projects that have directly involved students in the creation of those materials. We provide an overview of the various ways in which students have become involved in OER creation in partnership with faculty and librarians and discuss the impact these partnerships have had on student-faculty-librarian relationships and student engagement. Among these projects are an Hispanic linguistics open textbook created using only student-authored texts, student-generated test banks to accompany existing OER materials for a large-enrollment art history course, and several other projects in which hired student assistants are helping faculty to develop content for open textbooks. This article helps to address a gap in the literature by providing transparency regarding the personnel, costs, and workflow for Ohio University Libraries’ OER initiatives and addressing potential areas of concern surrounding student labour.
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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 | 0.027 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| 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".