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Record W1587238562 · doi:10.1155/2015/165165

Enhancing the Cognitive and Learning Skills of Children with Intellectual Disability through Physical Activity and Edutainment Games

2015· article· en· W1587238562 on OpenAlexaff
Amal Dandashi, AbdelGhani Karkar, Sawsan Saad, Zaara Barhoumi, Jihad Mohamad Alja’am, Abdulmotaleb El Saddik

Bibliographic record

VenueInternational Journal of Distributed Sensor Networks · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Ottawa
FundersQatar National Research FundFonds National de la Recherche Luxembourg
KeywordsComputer scienceIntellectual disabilityInterface (matter)CognitionMultimediaProcess (computing)Cognitive disabilitiesTangible user interfaceHuman–computer interactionUser interfacePsychology

Abstract

fetched live from OpenAlex

This work introduces an edutainment system specifically designed to help children with intellectual disability (ID) in order to allow them to have an enhanced and enjoyable learning process and addresses the need for integrating physical activity into their daily lives. The proposed system consists of a multimedia technology based games with a tangible user interface. The edutainment system was tested on 77 children with different intellectual disabilities (IDs). The mildly disabled groups achieved best results in terms of scores and coordination, but all the observed groups exhibited high motivation levels. The results proved that the system had very positive effects on the children, in terms of cognition and motivational levels, especially as the children became more physically active in the classrooms. Instructors also expressed willingness to incorporate the edutainment system into the classroom on a daily basis, as a complementary tool to conventional learning.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.016
GPT teacher head0.314
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations46
Published2015
Admission routes1
Has abstractyes

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