Using an Instructional Fluency Approach to Teach Addition Skills in a Pupil Referral Unit: A Pilot Study
Bibliographic record
Abstract
Pupil referral units (PRUs) in Wales accommodate children who present with a range of difficulties that cannot be managed within a mainstream setting. Many children attending PRUs in Wales do not develop the numeracy skills that they need to support their learning across the curriculum. In an effort to teach and assess addition skills, the authors assessed the effects of using a combination of direct instruction (DI) and precision teaching (PT) in a PRU. Over six school weeks, we worked with five children (aged 7 to 10 years) on a 1:1 basis through the Corrective Mathematics addition curriculum (Engelmann and Carnine, 2005). Following each lesson, the children completed an individualised fluency assessment, which we tailored to their needs using PT methods. We collected baseline and follow- up data using the Test of Early Mathematics Ability (TEMA-3), the Wide Range Achievement Test (WRAT-4) and the Corrective Mathematics placement test. We also interviewed the children post- intervention to gain insight into their experience of the approach. The results provide evidence to support the use of an instructional fluency approach in a PRU setting to help children develop early mathematics skills, particularly for children who engaged in the sessions regularly. Due to the small sample size, the results of this study have limited generalisability but may help shape future research investigating effective strategies for teaching mathematics in PRUs.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".