Comparing Spoken Versus iPad-Administered Versions of a Narrative Retell Assessment Tool in a Practice-Based Research Partnership
Bibliographic record
Abstract
PURPOSE: In the current age of greater digital delivery of services, it is important to examine the validity and differences between spoken and digital delivery of materials. The current study is a practice-based research partnership between school-based speech-language pathologists (SLPs) and researchers, evaluating presentation effects and validity of a narrative retell assessment tool created by SLPs. METHOD: in three in-person story presentation conditions administered 1 week apart: spoken, iPad with audio-recorded natural rate of speech, and iPad with slow rate of speech. This was followed by 10 comprehension questions related to story events. Children also completed the Story Retelling subtests from the Test of Integrated Language and Literacy Skills (TILLS). RESULTS: Children recalled significantly fewer events in the spoken condition compared to audio-recorded iPad conditions. No significant effect of speaking rate was found. Presentation condition and rate did not affect performance on comprehension questions. Correlations among retell measures and corresponding subtests on a standardized language test ranged from weak to strong, providing some evidence of concurrent validity. CONCLUSIONS: This practice-based research partnership provided valuable insight into differences in delivery modality as well as the validity of a school-based SLP created narrative retell assessment tool. This study found that rate did not impact recall of events or performance on comprehension questions. Additionally, children performed better on narrative retell measures when stories were told using an iPad. This highlights the potential for iPad delivery as an option in narrative retell tasks. Finally, this study provided an initial examination of the Narrative Evaluation Tool's validity, finding the tool captures ability to recall narrative events; however, future studies are needed to examine the tool's validity as a measure of narrative comprehension. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25260910.
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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 | 0.038 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".