Priorities and possibilities for teacher professional learning in a (post)COVID-19 era
Bibliographic record
Abstract
This paper aims to strengthen understandings of both the current status of and future directions around teacher professional learning in Canada. Bearing in mind the significant shifts in teacher professional learning as a result of the pandemic, the paper utilises an existing framework around the features of high-quality professional learning from a prior study to analyse recent trends in teacher learning needs and professional learning modalities to identify priorities and possibilities for the future of teacher learning in the (post) COVID-19 era. Particular attention is paid to what we refer to as the COVID conundrum. On the one hand, the proliferation of online learning opportunities necessitated by the pandemic resulted in an increase in access for educators who have not been served well by traditional face-to-face models of professional learning. By the same token, however, the sheer volume of learning opportunities, combined with the privatisation and globalisation of online teacher learning (Educational International 2020a), presents challenges around ensuring professional learning is relevant and contextualised to local teaching and learning contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".