Soft Landings Creating a Safe Place for Teachers to Take Risks While Learning
Bibliographic record
Abstract
Leaders have a significant influence on student learning and are responsible for leading teacher professional learning in schools. While principals often lead teacher learning, others assist in this role. With the recent pandemic, it is clear there is a need for continued teacher learning. Extensive research on professional development has been conducted and is well known to be a strategy for improving teacher learning. While professional development offers short-term opportunities, a focus on professional learning in schools emphasizes sustained, ongoing experiences that engage teachers in inquiry around their practices. This mixed study explored the impact of sustained professional development. Semi-structured interviews and focus groups were conducted and analyzed. Documents were identified for quantitative analysis and then integrated with qualitative data. Findings showed the notion of soft landings, as referred to by participants, a culture where it was safe to risk and try to implement ideas they were learning. This culture was an important aspect of this sustained and ongoing experience for teachers. Key leadership practices nurturing the culture of soft landings were developing collective commitment, encouraging risk-taking, promoting collaboration, and providing individualized support. This study offers insights into how leadership can play a role in providing sustained experiences to create a school culture that nurtures teacher learning. This article holds significance to those leading teacher learning in schools and those interested in utilizing leadership practices that attend to building a culture of soft landings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".