Culturally Relevant and Responsive Practices in Literacy Instruction
Bibliographic record
Abstract
This qualitative study focused on investigating the ways in which educators utilise culturally relevant and responsive practices (CRRP) in order to improve their literacy instruction. Through a detailed literature review, as well as direct, in- person or telephone, interviews with three experienced teachers from various school boards in the Greater Toronto Area, four important themes emerged. The first theme: recognising, celebrating and learning from cultural diversity in the classroom; describes the need for safe and respectful settings for cultural diversity to be acknowledged. The second theme: teaching literacy skills to students; educators should give their students engaging tasks to spark interest in literacy. The third theme: using culturally relevant and responsive practices to develop literacy; educators should utilise cultural texts within their instruction. The fourth theme: teacher identified challenges (and benefits) to the approach; emphasises that teachers may face lack of support and resources when trying to integrate CRRP into their instruction, but positive relationships are built with students and their families as a result. Through interviewing educators, it was concluded that these practices could be utilised in classrooms but teachers would still need proper training and support. There would be a call for culturally relevant and responsive changes within the current curriculum and proper professional development for educators to be properly prepared to meet the cultural and literacy needs of their students. Based on the findings, this research project expresses how educators can use culturally relevant and responsive practices to guide their own teaching and improve their literacy instruction.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".