Comparing community-based reading interventions for middle school children with learning disabilities: possible order effects when emphasizing skills or reasoning
Bibliographic record
Abstract
Introduction There is an abundance of community-based reading programs for school-age children who are struggling learners. The aim of this study was to compare two community-based programs (i.e., skill and reason-based programs) and to analyze any complementary benefits. Methods In this randomized cross-over study, 20 children completed two 8-week literacy intervention programs. The skills-based program, Leap to Literacy, focused on explicit teaching and repeated practice of the five key components of literacy instruction (phonemic awareness, phonics, fluency, vocabulary, comprehension). The reason-based program, Wise Words, focused on morphological knowledge, hypothesis testing, and critical thinking. Results Results revealed study-wide improvements in phonemic awareness, nonword reading, passage reading accuracy, spelling words and features, and affix identification. There were consistent program by program order effects with robust effects of completing the skills-based program first for phonemic awareness, the reason-based program first for passage reading accuracy, and both programs for affix identification. A significant increase in an oral language measure, recalling sentences, was observed for the group who completed the reason-based program first, although they also started off with a lower initial score. Discussion Findings indicated improvements from participating in either program. The observed order effects suggest potential additive effects of combining reason- and skills-based approaches to intervention.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".