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Record W7025267603

Towards appropriate sensory products for learners with learning problems: a case study-based review

2013· article· en· W7025267603 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2013
Typearticle
Languageen
FieldEngineering
TopicRadiation Effects in Electronics
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Intervention (counseling)Remedial educationPsychological interventionCurriculumPerceptionIdentification (biology)Primary education
DOInot available

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.252
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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