An Investigation of the Relationship between Professional Learning Community Practices and Student Achievement in an Eastern Canadian School Board.
Bibliographic record
Abstract
Abstract The participants in this study were teachers and students of a large school district located on the east coast of Canada. We distributed surveys to 1514 teachers across 84 schools with1423 usable surveys having been completed for a return rate of 94%. At the time of the second data collection, three years later, 78 schools with a total of 1574 teachers agreed to participate. Teachers’ perceptions of their schools as professional learning communities were assessed using 32 items in a 5-point Likert format. The responses to the 2006 survey were factor analyzed using ML estimation. A number of different factor structures were tested, including a single factor model and a second-order factor analysis. In spite of a heavy emphasis by school board leaders and the collaborative development, introduction, and implementation of PLC practices before and during the study period, no significant measurable effect on student learning was identified. While this particular study reports only the relationship between PLC practice usage and reading achievement, Hurley, Sheppard, and Seifert are also investigating two other areas of efficacy in this line of study; one that investigates the relationship with high school Mathematics achievement and one that examines school size and student achievement as mediated by PLC practice
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".