Inclusive Achievement Testing for Linguistically and Culturally Diverse Test Takers: Essential Considerations for Test Developers and Decision Makers
Bibliographic record
Abstract
Substantial growth in the numbers of English language learners (ELLs) in the United States and Canada in recent years has significantly affected the educational systems of both countries. This article focuses on critical issues and concerns related to the assessment of ELLs in U.S. and Canadian schools and emphasizes assessment approaches for test developers and decision makers that will facilitate increased equity, meaningfulness, and accuracy in assessment and accountability efforts. It begins by examining the crucial issue of defining ELLs as a group. Next, it examines the impact of testing originating from the No Child Left Behind Act of 2001 (NCLB) in the U.S. and government‐mandated standards‐driven testing in Canada by briefly describing each country's respective legislated testing requirements and outlining their consequences at several levels. Finally, the authors identify key points that test developers and decision makers in both contexts should consider in testing this ever‐increasing group of students.
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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 | 0.137 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".