Using Culturally Relevant Pedagogy to Support ELL Literacy & Language Acquisition
Bibliographic record
Abstract
Developing student literacy is crucial to students’ academic and overall life success (Cummins, 2000a, p. 53), but the literacy acquisition of English Language Learners is hindered by lack of funding for ELL support, teachers who lack knowledge of ELL support, and inability to engage with literacy materials they are given in the classroom (Jones & Carter, 2010). Research says that Ontario teachers feel they are receiving insufficient training to support ELL students, despite the fact that Ontario ELL student populations average at 8% per school (and as high as 92% in some schools [People for Education, 2013].) In addition to a lack of training, it has also been reported that many teachers feel reluctant to work with “low-proficiency ELLs,” and hold “misconceptions about the processes of second-language acquisition [as well as] assumptions (positive and negative) about the race and ethnicity of ELLs” (Reeves, 2006). Research conducted on how to effectively support ELL literacy and language acquisition has shown the effectiveness of enacting Culturally Relevant Pedagogy (CRP) (Ladson-Billings, 1995). However, despite the documented success of teachers who have adopted CRP, it remains largely an unrealized educational ideal. This study explores how a sample group of primary-level teachers use CRP to effectively support student literacy (ELL or otherwise), and the personal and professional factors that influence their decision to actively enact CRP in their practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".