Formal and Informal Approaches to the Language Assessment of Deaf Children
Bibliographic record
Abstract
Abstract From the moment deaf children are first identified, language development is a primary educational goal, and meaningful assessment of language skills is the cornerstone upon which initial placement and subsequent educational programming rests. In this connection, language assessment should be tailored to respond to specific diagnostic questions and to meet individual language and learning needs. However, few tests can be used reliably with this population; thus early interventionists, educators, and clinicians need to proceed cautiously when planning for, conducting, and interpreting findings of language assessments of deaf children. This chapter discusses some of the important issues surrounding the language assessment of deaf children, including issues that influence the selection of approaches and measures, assessment procedures, and interpretation of findings. Language assessment typically focuses on aspects of semantic, syntactic, or pragmatic development, and both formal (or standardized or “product-oriented”) and informal (or “process-oriented”) approaches and measures contribute to the language assessment process within and across these domains. Formal measures include instruments developed for and normed on typically hearing children and adapted for use with deaf children, as well as the comparatively smaller number of instruments designed specifically for use with children with hearing losses. In contrast, informal assessment is based on the assumption that language performance should be viewed in context and evaluated over time against the child’s own baseline. Overall, the aspect of language under investigation should be assessed using a variety of formal and informal approaches, and findings should be integrated both within and across domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".