Testing the Feasibility and Impact of Train-the-Trainer Delivery for a Peer Tutoring Reading Programme in Chile
Bibliographic record
Abstract
Peer tutoring through a structured low-cost approach to reading in pairs using the Paired Reading programme has resulted in attainment gains in reading in English (UK) and Spanish (Colombia), using developer-led training in schools. Given the continued issues with poor reading attainment globally, in preparation for the scalability of Paired Reading in the Global South, a train-the-trainer delivery programme was developed, implemented, and assessed using a matched study design including 6 classes and 98 Grade 6 students in a high-poverty region of Chile. The results indicate that the Paired Reading train-the-trainer programme (Latin American Spanish version) is feasible to implement in elementary schools in high-poverty areas in Chile and is capable of improving children’s reading ability as measured by an independently designed standardised reading assessment. Positive results were found (effect size d = +0.67, g = +0.66) for the children who engaged in the technique when assessed against a matched control group. The results indicate that this programme is now ready for assessment using a randomised controlled trial in Chile to test the effectiveness of using this more scalable method of delivery, including with standardised digital resources, for sustainable delivery in the Latin American region.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".