The effectiveness of a cognitive-plus-motivational reading intervention: A multiple-baseline study with four pupils at-risk for reading difficulties
Bibliographic record
Abstract
Aims We aimed to examine the impact of supplementing cognitive reading intervention with supports for reading motivation on the reading motivation (reading interest and self-efficacy) and reading fluency of four Year four pupils at-risk for reading difficulties. Method/rationale Case studies of four pupils at-risk for reading difficulties were conducted using a multiple-baseline across-participants design. The effects of a combined Cognitive-plus-Motivational intervention (experimental phase) were compared to those of a Cognitive-Only intervention (baseline phase) using probes for reading fluency, interest, and self-efficacy. Scores on each probe were plotted and analysed by combining visual analysis and the Process Control Chart method of analysis. Findings Results suggest that compared to a Cognitive-Only intervention, the Cognitive-plus-Motivational intervention improved the fluency of three participants, and the interest and self-efficacy of two out of four participants. Findings provided insight into individual patterns of response to the intervention, with the greatest impact on fluency observed for students with the lowest initial reading skills. Limitations Caution is needed in generalising findings due to the study’s small sample size, the lack of a control group and the potential presence of experimenter bias. Conclusions The findings presented here provide preliminary support for the benefits of supplementing reading intervention with instruction to foster reading motivation on the outcomes of pupils at-risk for reading difficulties and provide insight into patterns of individual response to motivational 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".