Open Educational Practices Advocacy: The Instructional Designer Experience
Bibliographic record
Abstract
Instructional designers are in a unique position to provide leadership and support for advancement of new technologies and practices. There is a paucity of research on current and potential roles of Instructional designers in incorporating and advocating for open educational practices at their higher education institutions. Against the background of emerging open educational practices, a survey and interviews were conducted with instructional design professionals to establish, from their experience and practice, their roles and potential for advocacy for open educational practices (OEP) including open educational resources (OER). Among the results of the analysis, it was found that while instructional designers have a strong awareness of and desire to advocate for OEP in their institutions, their ability to move forward was limited by perceived barriers such as lack of relevant mandates and professional workload recognition, policy development and funding, awareness and leadership support. In addition, there were gaps identified between what they most valued about OEP, such as implementing innovative pedagogies, and what they could actually initiate and advocate for in practice (adopt and support OER). They pointed to a lack of formal learning opportunities around OEP and expressed that their main sources of learning and support were of an informal nature, acquired through their networks and collaborations with peers.
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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 | 0.061 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".