Enhancing Academic Achievement for Students Living in Poverty Through Transformative Leadership, Instructional Practices and Professional Learning Communities
Bibliographic record
Abstract
This study utilized the mixed methods research to explore the correlation between academic achievement and poverty. The collection and analysis of quantitative data on reading, writing and mathematics; demographics (born outside of Canada, primary home language, special needs learners) and school community characteristics (e.g. family income) facilitated the identification of high performing schools (performing above 60% at levels 3 and or 4 in EQAO [Education, Quality and Assessment Office] reading, writing and mathematics at either Grade 3 or 6) serving economically disadvantaged students. The stratified sampling technique allowed for the selection of a subgroup representative of the sample under study. The purposive strategy enabled the selection of the most outstanding successes related to academic achievement and poverty. The qualitative data was used to explore transformative leadership, instructional practices and professional learning communities (PLCs) as possibilities for changing the trajectory of underachievement to achievement. From the data collected, analyzed, and presented, the researcher made the following conclusion: Schools in the sample experienced a higher level of academic achievement even though their placement on the Learning Opportunity Index (LOI) ranking was considered high. The findings have implications for policy development, leadership training, teacher education, and professional development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".