A randomized controlled trial of an early‐intervention, computer‐based literacy program to boost phonological skills in 4‐ to 6‐year‐old children
Bibliographic record
Abstract
BACKGROUND: Many school-based interventions are being delivered in the absence of evidence of effectiveness (Snowling & Hulme, 2011, Br. J. Educ. Psychol., 81, 1). AIMS: This study sought to address this oversight by evaluating the effectiveness of the commonly used the Lexia Reading Core5 intervention, with 4- to 6-year-old pupils in Northern Ireland. SAMPLE: A total of 126 primary school pupils in year 1 and year 2 were screened on the Phonological Assessment Battery 2nd Edition (PhAB-2). Children were recruited from the equivalent year groups to Reception and Year 1 in England and Wales, and Pre-kindergarten and Kindergarten in North America. METHODS: A total of 98 below-average pupils were randomized (T0) to either an 8-week block (x¯ = 647.51 min, SD = 158.21) of daily access to Lexia Reading Core5 (n = 49) or a waiting-list control group (n = 49). Assessment of phonological skills was completed at post-intervention (T1) and at 2-month follow-up (T2) for the intervention group only. RESULTS: = .070 (small effect size). Analysis of the 2-month follow-up of the intervention group found that all group treatment gains were maintained. However, improvements were not uniform among the intervention group with 35% failing to make progress despite access to support. Post-hoc analysis revealed that higher T0 phonological working memory scores predicted improvements made in phonological skills. CONCLUSIONS: An early-intervention, computer-based literacy program can be effective in boosting the phonological skills of 4- to 6-year-olds, particularly if these literacy difficulties are not linked to phonological working memory deficits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".