THE EXPERIENCES OF LITERACY AND NUMERACY COACHES IN IMPLEMENTING INITIATIVES TO RAISE STUDENT ACHIEVEMENT
Bibliographic record
Abstract
This thesis examines the initiative involving the implementation of elementary Literacy and Numeracy Coaches. Using a case study, I examined the challenges and opportunities with which Literacy and Numeracy Coaches were confronted. The study concluded that Literacy and Numeracy Coaches have great potential for improving student achievement and raising improved teaching strategies through professional development. However, due to several factors, including: vague role definition; implementation of too many initiatives at the same time; the shifting of the role due new initiatives encouraged by the Ministry and the board; and too little time for effective implementation, caused the coaches to be overloaded, and the schools to be unsure of the Coaches’ role, thus reducing the potential effectiveness of the Coaching initiative. This study has made a contribution to the limited research on Literacy and Numeracy Coaching in Canada and provided results that may help to inform future initiatives
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| 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".