Inattention and Response to the ABRACADABRA Web-Based Literacy Intervention
Bibliographic record
Abstract
Inattention is often associated with reduced response to reading intervention. This study explored attention as a predictor of individual variation in response to a free-access Web-based literacy intervention, ABRACADABRA (http://abralite.concordia.ca) in typical Grade 1 children. A randomized control design was used to contrast two interventions, a phoneme-based Synthetic and a rime-based Analytic Phonics approach, against a regular classroom control condition. Attention measured by parent ratings and a sustained attention task, was correlated with reading. Attention also predicted growth in blending and reading comprehension for students receiving only regular classroom teaching. However, in the most successful intervention, Synthetic Phonics, attention no longer predicted reading outcome. An omnibus analysis of effect sizes that combined all attention measures across all areas of literacy attainment improved by ABRCADABRA confirmed that there were significant differences between the regular classroom teaching control and the synthetic phonics intervention: Attention predicted significantly more variance in attainment in the control condition. These results suggest that the computer-based literacy intervention, ABRACADABRA, can influence the associations between literacy and attention and may support students at risk of reading and attention difficulties.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".