Orton-Gillingham Approach as an Online Intervention for Learners Diagnosed with Attention Deficit Hyperactivity Disorder (ADHD)-Specific Learning Disorder (SLD) in Mathematics: A Descriptive Case Study
Bibliographic record
Abstract
The purpose of this qualitative descriptive case study was to investigate whether the use of Orton-Gillingham Math as an intervention in teaching a child diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and Specific Learning Disorder (SLD) with impairment in mathematics lead to an improvement in addition skills. This study had a single participant chosen purposively based on the criteria namely diagnosis, arithmetic calculation level, and unfamiliarity with OG-Math intervention. The learner-participant was taught using the OG-Math approach which two main features are multisensory approach and concrete-representational-abstract progression via an online platform for twice-a-week over a period of four weeks. The pre and post evaluation of the learner-participant’s Developmental and Behavioral Pediatrician and the pre and post paper and pencil test which was composed of 75-item addition problems conducted by the teacher were used to determine the learner-participant’s basic operation addition fact knowledge prior and subsequent to the implementation of OG-Math online intervention. The data were analyzed using trustworthy thematic analysis and pattern matching. The analysis revealed that the learner-participant can already solve addition problems involving 3-digit addends with and without regrouping after the math intervention from only being able to solve single-digit addition problems. Hence, it is recommended to mathematics teachers to use OG-Math approach as an online intervention to children with ADHD and SLD with impairment in math to improve their addition skills.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".