Curriculum-Based Dynamic Assessment of Narratives for Bilingual Filipino Children
Bibliographic record
Abstract
PURPOSE: Speech-language pathologists need tools that can accurately estimate bilingual children's language abilities and thus help avoid misdiagnoses. This study addresses this need by investigating the accuracy of a novel curriculum-based dynamic assessment of narratives in distinguishing bilingual children with language difficulties (LDs) from children with typically developing (TD) language. METHOD: Participants comprised 34 Filipino-English bilingual children attending elementary school in English: seven with LDs and 27 with TD language. All children were assessed on narrative skills relevant to their school curriculum during a dynamic assessment involving a test-teach-test sequence. We then examined how accurately the children's scores on narrative tasks completed during the test phases, and on a modifiability rating scale completed during the teaching phase, discriminated the LD and TD groups. RESULTS: According to discriminant analyses, logistic regressions, and receiver operating characteristic curve analyses, the modifiability rating classified the children with 97.1% accuracy. Children's scores on the narrative measures following the teaching phase were also better at predicting language group than their initial scores, with the Test of Narrative Language-Second Edition (TNL-2) Narrative Language Ability Index score reaching 100% accuracy at posttest. CONCLUSIONS: The curriculum-based dynamic assessment of narratives shows promise at distinguishing TD language from LD in a group of understudied bilingual children that is rapidly growing in both Canada and the United States. The findings compare favorably to past studies of dynamic assessment and extend this work by integrating curricular goals to the narrative assessment process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".