Using Open Educational Practices to Support Institutional Strategic Excellence in Teaching, Learning & Scholarship
Bibliographic record
Abstract
This paper explores the integration of Open Educational Practices (OEP) into an institutional strategy to develop distinctive excellence in teaching, learning and scholarship. The institution in the case study is a public polytechnic university serving a metropolitan area in Canada. If emerging Open Educational Practices are to flourish at our university, support for OEP must integrate with and contribute to our broader efforts to clarify and enhance our strategic position. We have identified three focal points where our institution can focus attention in order to ensure that our use of emerging Open Educational Practices will best align with, contribute to, and benefit from our institutional strategy for distinctive excellence in teaching and learning: Opening up the pedagogy underlying exemplary OER, to enable a deeper faculty engagement in integrating and mobilizing diverse sources of knowledge in teaching; Opening up that process by which individual faculty improve teaching and learning, as a model for our students’ own engagements with knowledge; Opening up our collective faculty work in innovation networks, as a model for students and as a signature institutional strength and outcome. We summarize the rationale and planned next steps for each of these focal points, which are intended to cumulatively build on each other as a value chain to support the development of distinctive graduate capabilities as signature outcomes of our teaching and learning.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".