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Record W2766970018 · doi:10.5539/elt.v10n12p37

The Attitudes of Fifth and Sixth Graders in Kuwait Governmental Schools towards Recreational and Academic Reading in English

2017· article· en· W2766970018 on OpenAlexvenueno aff
Amel AlAdwani, Anaam Al-Fadley

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)PsychologyRecreationCurriculumMathematics educationSample (material)PedagogySocial psychology

Abstract

fetched live from OpenAlex

The current study is a quantitative research that examined the mean differences of the students’ attitude towards reading, based upon several demographic variables (such as gender, grade level and social media devices usage)The researchers used the Students’ Reading Attitude Survey (SRAS) as the dependent variable; the sample consisted of 812 young elementary students (from the 5th and 6th grade) randomly selected from public schools.The research findings revealed that Kuwaiti students possess favorable attitudes toward both leisure and academic reading. Girls showed more positive attitudes toward reading than boys did. Younger students from the 5th grade showed more positive attitude toward reading than those of the 6th grade. Nevertheless, the results indicated that having an account in Instagram, Snap chat, or YouTube, or possessing a smart device had a negative effect on attitudes towards reading.This study is expected to help curriculum designers, education policy makers, and English teachers to promote independent reading amongst school students and enable them to move beyond traditional books by encouraging them to form a community of life-long readers in the Arab world.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.350
Teacher spread0.329 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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