Achieving Literacy Equity by Practicing Excellence: Ontario Teachers’ Shift to Science of Reading Approaches
Bibliographic record
Abstract
Findings from the OHRC’s Right to Read (2022) Inquiry revealed that the approach to early reading used in many Ontario schools does not align with the science of reading and fails our most vulnerable students despite research on effective literacy instruction being widely available and well established. While the research on effective literacy instruction has been known to literacy teachers and researchers for decades, other teachers are being confronted with the realization that the literacy practices they have used for decades were not based in reading research and supportive of all children. The purpose of this study was to provide insight into the evidence-based instructional strategies Ontario teachers implementing the science of reading are utilizing in their classrooms. Using purposeful sampling, this study recruited three primary (K-3) literacy teachers who had additional training in evidenced-based literacy practices and began implementing these practices into their classrooms within the past five years. To answer the research questions, this study utilized a qualitative, multiple case study design. Findings include: 1) a number of strategies for teaching the five components of effective reading instruction, 2) that participants learned about the science of reading and the evidence-based approaches they implement from self-initiated learning, 3) that participants utilized their professional knowledge to plan, implement, and deliver evidence-based literacy instruction even without support from their school board, 4) that there is still a significant amount of variability in the way school boards across Ontario are responding to the Right to Read report and discussions surrounding evidence-based literacy practices, and 5) that teachers lack evidence-based resources and materials for implementing the science of reading. Further research is needed to determine how to: 1) support teachers shifting to evidence-based literacy instruction, 2) implement system wide professional development for Ontario educators, and 3) facilitate teacher buy in.
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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".