Assessing Oral Language When Screening Multilingual Children for Learning Disabilities in Reading
Bibliographic record
Abstract
Multilingual children represent a rapidly growing population of students in U.S. schools. However, identification of language and learning disabilities for students from different linguistic backgrounds is complex, leading to frequent misidentification of multilingual learners for special education. This article provides guidance on how special education teachers, speech-language pathologists, and other practitioners (e.g., school psychologists) can utilize each other’s expertise to accurately assess language and literacy skills of multilingual learners. Five key lessons learned from research on identification of language disorders are presented, along with discussion of why these are important when screening multilingual children for learning disabilities in reading. Specifically, there is a focus on considering children’s language background, regardless of English learner status, the importance of language ability for reading achievement, common pitfalls in using standardized assessments with multilingual learners, and linguistically sensitive assessment and scoring practices to be used with multilingual students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".