A Qualitative Study of Pedagogical consultants’ and teachers’ experiences and perceptions of PLCs
Bibliographic record
Abstract
Professional learning communities (PLCs) have become increasingly prevalent as a strategy for engaging teachers in school planning and distributing leadership function within schools in Quebec. While many scholars have praised the potential of PLCs, their effectiveness in practice has been called into question. This qualitative study offers a close examination of PLCs by exploring the experiences that teachers and pedagogical consultants have had in PLCs, and by outlining how these experiences have shaped their perceptions of PLCs. This research, consequently, seeks to contribute to the literature on professional learning communities through an exploration of PLCs within a particular context – an English minority school board within the province of Quebec. Data was collected through interviews with five participants, as well as from my reflective memos in order to address the research question: How has the attempt to implement PLCs in an English Language school board influenced the way their teachers and pedagogical consultants perceive PLCs? A constant comparison analysis of the participants’ interviews revealed three major themes, including nine sub-categories. The findings of the study suggest that even though the participants had generally negative experiences in their PLCs, they retained the belief that PLCs were a viable model to help improve student outcomes. Their experiences, furthermore, provided participants with a deepening understanding of PLCs including a recognition of where PLCs fail, and what needs to happen for PLCs to succeed. The study concludes with a series of questions that researchers or policy makers can use for future investigations of PLCs
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".