Middle-Out Approaches to Reform of University Teaching and Learning: Champions striding between the top-down and bottom-up approaches
Bibliographic record
Abstract
In recent years, Australian universities have been driven by a diversity of external forces, including funding cuts, massification of higher education, and changing student demographics, to reform their relationship with students and improve teaching and learning, particularly for those studying off-campus or part-time. Many universities have responded to these forces either through formal strategic plans developed top-down by executive staff or through organic developments arising from staff in a bottom-up approach. By contrast, much of Murdoch University's response has been led by a small number of staff who have middle management responsibilities and who have championed the reform of key university functions, largely in spite of current policy or accepted practice. This paper argues that the "middle-out" strategy has both a basis in change management theory and practice, and a number of strengths, including low risk, low cost, and high sustainability. Three linked examples of middle-out change management in teaching and learning at Murdoch University are described and the outcomes analyzed to demonstrate the benefits and pitfalls of this approach.
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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 | 0.041 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".