Post-COVID-19 Rooms of Our Own: Lessons Learned from Virtual Professional Development Projects Designed for Early Childhood Educators in Ontario
Bibliographic record
Abstract
This paper reflects and explores lessons learned during two professional learning series related to the pedagogical practices of early childhood educators (ECEs) in Ontario, Canada. Drawing on a comparative analysis of our observations, collaborative inquiries, and discussions, we underline post-COVID-19 conditions that change how we think, engage, and envision possibilities in professional learning. We discuss the way the use of technology at the intersection of time and space and carefully chosen pedagogical approaches pushed us to reconsider current practices used in the design of professional learning activities, the implementation, and the responses to educators’ learning. We focus on the way technology helped us envision and plan for virtual rooms as environments as third teachers. Trading the traditional professional workshop-like activities with fixed time boundaries for virtual café-style learning, introducing design thinking, distributed leadership, Indigenous world views, and rhizomatic wonderings, we discuss our decisions to change the directions of professional learning from focusing on skill development and transmission of knowledge to enhancing dispositions needed to become lifelong learners, innovators, and advocates. We conclude the paper with invitations and provocations for educators, academics, researchers, and regulatory bodies to further discuss professional learning activities for the early childhood education (ECE) community in Ontario. In brief, we focus on removing the dividing practices between professional learning activities and pedagogical approaches as a starting point to envision possibilities in Ontario’s ECE field.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".