A Student Co-Creation Lab As A Sustainable System For OER Development
Bibliographic record
Abstract
Open educational resources (OER) are digital and non-digital resources for teaching, learning, and research that are available for no cost and licensed with various open licenses, permitting their widespread modification and use. While these resources afford benefits to post-secondary students in terms of reduced cost, ease of access, and potential for customization, faculty members in Ontario, Canada face many barriers to creating these resources for their classes. While many post-secondary institutions have engaged in some form of OER-development initiative, the initiatives are typically not sustainable in the long term. This study explores the model, including the tools, techniques, and workflows, of the OER Lab at Ontario Tech University, a student-run, staff-managed group which provides OER development services for faculty at the university. Through a series of qualitative interviews framed by design-based research, the study characterises the capacity-building techniques of the OER Lab model. The research discusses setting expectations, providing training and experiential learning opportunities, and using existing tools to back the Lab, while engaging critical stakeholders to provide support for the initiative on campus. In addition, the study discusses how imbuing the OER-development work with key values reinforces its alignment with institutional vision, further enhancing sustainability. Through the capacity-building initiative of the OER Lab model, a sustainable approach to OER development can be explored at other campuses in Ontario and beyond.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".