Sustainable Problem Solving: Reducing Barriers for OER Adoption
Bibliographic record
Abstract
Open Education Global Conference 2023 (Edmonton, Alberta, Canada) session description. Are you thinking about new strategies or service models for helping educators find OER for their courses? At this presentation session (25 minutes), come learn about one approach a Canadian research institution has taken to help reduce hurdles faculty and instructors face when adopting Open Educational Resources (OER) into their courses. We will first highlight some of these barriers, followed by strategic services we provide at the University of Calgary’s Libraries and Cultural Resources (LCR) to reduce these barriers for educators. The first service we will discuss is providing an OER by Discipline Guide, an ongoing curated resource guide of OER organized by discipline for faculty who prefer to independently review previously evaluated OER within their subject area. Similar guides are commonly used across Canadian higher education institutions to curate potential OER organized according to their institution with access provided through Pressbooks. Pressbooks is a publishing platform based on WordPress used by many higher education institutions globally for developing, adapting, and hosting dynamic, interactive Open Education Resources. The second service strategy for supporting educators to adopt OER is the Open Course Materials Matching Service (OCoMMS). This service can be used by faculty who have either reviewed the OER by Discipline Guide and not found relevant OER on their subject, or by those who would prefer a more targeted service where library staff search for and curate potential OER relevant to their specific course. In order to support these services at LCR, we utilize the strengths and experience of our support staff as the strategies and practices for providing reference services are also similarly applied within the Open Course Materials Matching Service. We will highlight the benefits of having our support staff assist with OER finding services managed by the Open Education Librarian and our current service workflows. At the University of Calgary, this staffing model is a crucial piece in supporting the sustainability and growth of our OER services as interest in OER adoption continues to increase at our institution.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".