Open Course Materials Matching Service (OCoMMS): Reducing Barriers and Building Sustainability for OER Adoption
Bibliographic record
Abstract
Open Education Global Conference 2023 (Edmonton, Alberta) session description. Are you looking for a new approach to supporting educators in finding OER to adopt into their courses? In this lightning talk (15 minutes), learn about an Open Course Materials Matching Service (OCoMMS) model a Canadian research institution has introduced to help reduce hurdles faculty and instructors face when adopting Open Educational Resources (OER) into their courses. We will first highlight some of the barriers educators experience when starting their journey in finding OER to adopt, followed by outlining the Open Course Materials Matching Service provided by Libraries and Cultural Resources (LCR) staff at the University of Calgary. We will discuss what OCoMMS is, the service’s workflow, and the staffing model used within this service. Library staff supporting this service utilize a course’s outline or syllabus as a guide in conducting a targeted search for finding relevant OER which is provided to educators through a curated list they then evaluate for potential adoption. 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 searches within the Open Course Materials Matching Service 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 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".