Dynamic assessment of narratives: Case studies of bilingual Filipino kindergarteners with language difficulties
Bibliographic record
Abstract
The goal of the present study was to investigate the performance of four Filipino–English bilingual kindergarteners with language difficulties on a dynamic assessment of English narratives. Using a multiple case-study design, we examined the children's modifiability by detailing their responsiveness, level of skill transfer, and the amount and type of effort the examiner expended during two mediation sessions. We also assessed the children's narrative skills using the Test of Narrative Language—second edition (TNL-2). During mediation, similarities among the four children included difficulty answering wh-questions, little transfer of the newly learned skills, frequent focus on the story problem, and the need for continual prompting and repetition. However, the children showed individual learning styles, behavior when learning, and learning potential, prompting the examiner to apply different strategies to support their individual abilities. Gains on the TNL-2 were also negligible, consistent with the modifiability findings, apart from one child who showed improvement in comprehension scores. The case studies provide novel information regarding the narrative skills of Filipino bilingual children with language difficulties, an under-researched population. They can help guide expectations of such children during a dynamic assessment and suggest mediation strategies that clinicians could incorporate into their practice with other groups of bilingual children.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".