Leveraging a Diverse Collaboration in Tertiary Education to Develop Capability for Workplace Innovation
Bibliographic record
Abstract
Recent developments in tertiary education are demonstrating teaching and learning methods to develop students’ capability for employee-led Workplace Innovation. In this article, we describe an international collaboration to develop shared learning resources and activities in workplace innovation for adaptation in diverse tertiary education contexts. We are intentionally seeking out additional collaborating institutions that differ in mission, size, location and student demographics, to leverage our team’s diversity and encourage innovation. When shared learning resources and activities are to be used in a diverse contexts, some core principles underlying instructional success must also be shared in order to ensure adaptations do not remove key properties. We outline four instructional principles underlying the learning design and illustrate how these principles are applied in our current learning resources. We then describe some of the ways that these shared resources have been adapted for different tertiary education environments. We also discuss some of the benefits emerging from the collaboration, including how the inclusion of new resources targeting specific work domains and the transfer of new teaching and learning ideas across contexts. We conclude by describing some of the ways we are also collaborating with workplace partners, to ensure that our graduates have the capabilities needed to contribute to workplace innovation practice and to help advance the workplace innovation capability of their own employees.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".