Exploring the validity and reliability of online assessment for conversational, narrative, and expository discourse measures in school-aged children
Bibliographic record
Abstract
The COVID-19 pandemic has created novel challenges in the assessment of children's speech and language. Collecting valid data is crucial for researchers and clinicians, yet the evidence on how data collection procedures can validly be adapted to an online format is sparse. The urgent need for online assessments has highlighted possible the barriers such as testing reliability and validity that clinicians face during implementation. The present study describes the adapted procedures for on-line assessments and compares the outcomes for monolingual and bilingual children of online and in-person testing using conversational, narrative and expository discourse samples and a standardized vocabulary test. A sample of 127 (103 in-person, 24 online) English monolinguals and 78 (53 in-person, 25 online) simultaneous French-English bilinguals aged 7–12 years were studied. Discourse samples were analyzed for productivity, proficiency, and syntactic complexity. MANOVAs were used to compare on-line and in-person testing contexts and age in two monolingual and bilingual school-age children. No differences across testing contexts were found for receptive vocabulary or narrative discourse. However, some modality differences were found for conversational and expository. The results from the study contribute to understanding how clinical assessment can be adapted for online format in school-aged 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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".