Learning and leading through COVID-19: Surprising findings from a year of disrupted field experience
Bibliographic record
Abstract
When the COVID-19 pandemic necessitated the closure of Kindergarten-Grade 12 schools in Alberta, the authors, who are the Directors of Field Experience, at this local university saw this disruption as both a challenge and an opportunity (Danyluk, 2022). Over 400 preservice teachers were scheduled to begin their in-school practicum two days after the announcement of school closures. While most Bachelor of Education programs in Canada halted or postponed their field experience programs, the authors decided to move forward with an online practicum course. This chapter describes how we used collaborative professionalism (Hargreaves & O’Connor, 2018) to restructure field experience in response to the pandemic and the impact it had on student and field instructor learning. A community of practice was initiated by the Directors of Field Experience to support the instructors in the implementation of the online course and to come together as a community of learners in support of one another during this complex time. Survey and anecdotal data will be shared to illuminate the positive influence the pivot to the online field course had on students and instructors as well as the challenges we encountered as we navigated these uncharted waters as educational leaders.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.021 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".