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Record W4416723807 · doi:10.3390/educsci15121590

Testing the Feasibility and Impact of Train-the-Trainer Delivery for a Peer Tutoring Reading Programme in Chile

2025· article· en· W4416723807 on OpenAlexfundno aff
Maria Cockerill, Pelusa Orellana, Allen Thurston

Bibliographic record

VenueEducation Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersEconomic and Social Research CouncilQueen's UniversityQueen's University Belfast
KeywordsReading (process)Peer tutorTest (biology)Reading comprehensionEducational attainmentControl (management)Multilevel modelStandardized test

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.150

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.447
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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