Interactive book-reading to improve inferencing abilities in kindergarten classrooms: A clinical project
Bibliographic record
Abstract
Inferencing abilities are crucial to development of reading comprehension. However, few studies addressed those abilities in interventions promoting early literacy skills, especially in kindergartners. The aim of this study was to measure the efficacy of an interactive book-reading intervention targeting inferencing abilities, delivered by a school-based speech-language pathologist (SLP) in whole group kindergarten classes. Two hundred and forty-nine 5-year-old kindergartners from low socio-economic settings were quasi-randomly assigned to either one of the experimental groups (EG1 and EG2) or an active control group (CG). EG1 received a 7-week interactive book-reading intervention followed by a 7-week period where it was up to the teachers to implement aspects of the intervention in their teaching or not. EG2 received the 7-week interactive book-reading intervention only and the active control group received an initial workshop only. Three subtests targeting (1) causal inferences during book-reading, (2) causal inferences in a formal task, and (3) referential inferences in a formal task were performed at pre- and post-intervention assessments. There was a significant Time × Group interaction effect for the first subtest indicating an advantage for EG1 compared to CG over time. EG2 appeared as an intermediary group as its results were not different from EG1 and showing only a trend toward significance ( p = 0.064) when compared to CG. There was no significant Time × Group interaction effect for the second subtest. A significant Time × Group interaction effect was present for the third subtest, EG1 and EG2 showing larger improvement than CG.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".