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Record W2080891487 · doi:10.5539/ass.v7n10p44

The Relationship between Cognitive Processing and Reading

2011· article· en· W2080891487 on OpenAlexvenueno aff
Ooi Boon Keat, Khaidzir bin Hj. Ismail

Bibliographic record

VenueAsian Social Science · 2011
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)CognitionPsychologyReading comprehensionDevelopmental psychologyEffects of sleep deprivation on cognitive performanceCorrelationCognitive skillCognitive psychologyTest (biology)LinguisticsMathematics

Abstract

fetched live from OpenAlex

This study discusses the Cognitive Assessment System (CAS) in assessing cognitive processing. CAS is capable of measuring not only the cognitive ability and process but also of able to determine the deficits in relation with specific reading skills. Therefore, this study aims to examine the relationship of PASS cognitive processes with the reading components of Wide Range Achievement Test-4 (WRAT-4). The CAS and WRAT-4 were administered to a hundred primary standard three Malaysian children but based on American norms. The full scale of CAS mean scores was 98 which is the average level of cognitive processing among the subjects. While the WRAT-4 reading components showed the mean score of overall reading performance was 90 which slightly reach the average level. Correlation analysis revealed that cognitive processes were significantly correlated with overall reading performance with r=0789, p<0.01. Simultaneous and Planning processing had quite strong relationship with the overall reading performance which was r=0.724, p<0.01 and r=0.676, p<0.01 respectively. However, Successive processing had low score of r = 0.393, p < 0.01 for the correlation with reading comprehension. CAS is one of the alternatives in providing such capabilities in measuring cognitive processes that could predict reading deficits and relevant to identify performance on reading-related skills.

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: none
Teacher disagreement score0.655
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.204
GPT teacher head0.457
Teacher spread0.253 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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