Open is an Invitation: Exploring Use of Open Educational Resources with Ontario Post-Secondary Educators
Bibliographic record
Abstract
abstract: During the 2017-2018 academic year, I worked as Program Manager for a government-funded post-secondary organization in Ontario, Canada. A core part of my professional role was creating awareness and increasing the use of open educational resources (OER) in partnership with Ontario educators. I conducted this work with the support of colleagues and OER advocates at public colleges and universities. Collectively, we focused on the use of OER as an opportunity to: (a) reduce the cost of post-secondary resources, (b) diversify the types of resources used in teaching and learning, and (c) explore opportunities to create assessments and activities that empowered learners as co-creators of knowledge. Alongside my professional role during this year, I engaged in a mixed-methods action research study using change management strategies and Ajzen’s (1991) Theory of Planned Behavior. The purpose of the study was to determine the usefulness of an awareness and support strategy designed to increase the use of OER among post-secondary educators in Ontario.\n\nFor many of the participants in the study (n = 38), OER were new elements in their teaching practice. I engaged in focused and meaningful dialogue with them as part of professional development sessions in order to fully explore their perspectives about use of OER. I chose two facilitation designs as the action of my action research. The first was a pair of face-to-face workshops, and the second was an open online course commonly called a MOOC (massive open online course). These were the interventions (and innovations) for the study. From the perspective of the participants, the awareness and support strategies were determined to be useful for increasing their use of OER.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".