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Record W3176999683 · doi:10.2196/29142

The Effectiveness of Physical Education Games on Mathematics Achievement in a Sample of Students with Intellectual Disabilities (Preprint)

2021· article· en· W3176999683 on OpenAlexvenueno aff
Tariq A. Alsalhe, Nicola Luigi Bragazzi

Bibliographic record

VenueJMIR Serious Games · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual disabilityMathematics educationTest (biology)PsychologySample (material)PreprintComputer science

Abstract

fetched live from OpenAlex

UNSTRUCTURED: Background: Innovative techniques for teaching to students with intellectual disabilities are applied across the world, since conventional methods may work less efficaciously for them. In this investigation, an innovative method was applied to teach simple mathematics problems to students with intellectual disability. Objective: The purpose of the study was to determine the effectiveness of physical education (PE) games on mathematics achievements in a sample of students with intellectual disabilities in Riyadh, Kingdom of Saudi Arabia. Method: Participants of this study were 34 students with intellectual disabilities from inclusive middle school in Riyadh city. Participants were randomly recruited and, based on severity of their intellectual disability, allocated to an experimental and a control group. The former studied mathematics in PE classes, whereas the control group studied mathematics in pure mathematics classrooms. Results: Results showed significant improvements in post- versus pre-test in both groups. However, participants in the experimental group reported higher improvements compared to the participants in the control group. Conclusions: The present investigation seems to recommend the importance of using PE games during classes to improve learning skills, especially mathematics ones.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0000.000

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 head0.013
GPT teacher head0.358
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2021
Admission routes1
Has abstractyes

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