Towards appropriate sensory products for learners with learning problems: a case study-based review
Bibliographic record
Abstract
Children with learning problems (LP) 1 experience learning differently in the classroom environment (Speece, Case and Molloy 2003) and may require an adapted curricula and form of assessment. LP may be caused by a genetic predisposition, prenatal injury and/or various neurological and other general medical conditions.\nBradley, Danielson and Hallahan (2002) as well as McNamara (2004) advocate the early identification of children with LP, ideally within the primary grades in order to improve treatment effectiveness. Play is one of the interventions that a remedial teacher may introduce into a classroom. During play, children often use inanimate objects rather than verbal utterances to convey their feelings, beliefs and perceptions about themselves and their world (Schoeman and Van der Merwe 1996). Teachers involved with special education help these children through intervention strategies once LP has been identified. A wide variety of mediums, which can include interaction with educational toys, can be used as intervention strategies in a classroom or playroom environment. Luckin, Connolly, Plowman and Airey (2003) reported that interactive toy technology has the potential to stimulate children with LP. Two separate studies highlighted the need for developing interactive toys. These studies found that children with developmental disabilities respond better to these types of toys during play sessions (Bambara, Spiegel-McGill, Shores and Fox 1984; Hsieh 2008).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".