Building a culture of innovation in learning and teaching technologies through an innovators group
Bibliographic record
Abstract
Innovation was seen as crucial for universities even before the COVID-19 global pandemic, to widen participation within massification strategies, to deliver graduates that meet the needs of economies and to justify increased fees in response to reduced government funding. Innovation will become increasingly important as universities contribute to the post-pandemic recovery of their communities and nations and need to find new avenues for research funding with less reliance on governments. In this paper, we describe our first steps towards developing a culture of innovation in learning and teaching across science, health, and engineering disciplines by bringing together and empowering a group of mostly junior academics. We describe achievements of this group, indicate key success factors, and discuss next steps.
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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 | 0.048 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".