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Record W2921580725 · doi:10.1177/0008417419826114

Relationship between body functions and Arabic handwriting performance at different acquisition stages

2018· article· en· W2921580725 on OpenAlexvenueno aff
Abeer Salameh-Matar, Naser Basal, Naomi Weintraub

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsnot available
Fundersnot available
KeywordsHandwritingAutomaticityLegibilityCopyingComputer sciencePsychologyReading (process)ArabicDreyfus model of skill acquisitionSpeech recognitionNatural language processingCognitive psychologyLinguisticsArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

BACKGROUND.: The written languages and handwriting acquisition stages place different demands on the writer. Therefore, the relationship between body functions and handwriting performance may vary in different languages and acquisition stages; yet these demands have not been studied in the Arabic language. PURPOSE.: We examined the relationship between linguistic, visual-motor integration (VMI), and motor coordination (MC) functions and Arabic handwriting at two handwriting acquisition stages. METHOD.: This study used a cross-sectional and correlative design. Second- ( n = 54) and fourth-grade ( n = 59) students performed tasks examining reading, handwriting automaticity, VMI, MC, and copying a text. FINDINGS.: Handwriting automaticity significantly explained the variance in handwriting speed in both grades, in addition to the VMI in second grade and the MC in fourth grade. Enhanced performance in the VMI increased the likelihood of having good legibility in second but not in fourth grade. IMPLICATIONS.: Similar to other languages, the body functions related to Arabic handwriting vary at the different acquisition stages. Handwriting evaluation should be adjusted to students' acquisition stage.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.146
GPT teacher head0.383
Teacher spread0.237 · 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 designObservational
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

Citations8
Published2018
Admission routes1
Has abstractyes

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