Examining Teachers' Practices with ELLs: Equity in Assessment Through Socially and Culturally Informed Practices
Bibliographic record
Abstract
This project investigates how three teachers implement socially and culturally informed assessment practices for supporting their English language learners (ELLs). Through in-depth interviews with these teachers, this qualitative research study uses the framework of culturally relevant pedagogy (CRP) to explore their everyday assessment practices. The findings are presented as three case studies. A cross-case analysis where the findings are situated within the literature is also presented. The case studies examine teachers’ assessment practices and challenges; the role of identity in assessment; their engagements and challenges with culturally relevant content; and the impact of community on assessment. These teachers provide valuable insights into what culturally relevant assessment can look like in practice; for example, connecting content to students’ cultural and linguistic identities and engaging in discussions of social justice. There must be greater dialogue about the diverse cultural and linguistic encounters shaping Toronto’s classrooms. By bringing questions of culture into discussions of assessment, this research calls for greater attention to the cultural shifts that are rapidly changing the make-up of Toronto’s schools.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| 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".