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Record W2014250452 · doi:10.5539/elt.v7n8p124

Reading Trends and Perceptions towards Islamic English Websites as Teaching Materials

2014· article· en· W2014250452 on OpenAlexvenueno aff
Zurina Khairuddin, Azimah Shurfa Mohammed Shukry, Nurshafawati Ahmad Sani

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsnot available
FundersUniversiti Sultan Zainal AbidinInternational Islamic University Malaysia
KeywordsReading (process)IslamPsychologyPerceptionMathematics educationEnglish languagePedagogySet (abstract data type)LinguisticsComputer science

Abstract

fetched live from OpenAlex

This paper is a study of the reading trends and perceptions of Muslim Malaysian undergraduate students towards Islamic English websites as pedagogical materials in English language classrooms. Data was collected through a set of questionnaires to 180 students from the International Islamic University Malaysia (IIUM) and Universiti Sultan ZainalAbidin (UniSZA). The findings revealed that the students were self-motivated to read the materials to gain spiritual knowledge and to use the knowledge to deal with personal challenges. It also shows that the students recommend that the materials are used for pedagogical purposes in the learning of the English language. The study proposed that texts that bring enjoyment, inspiration, spiritual knowledge and personal development are used as reading materials in English lessons for Muslim students. This would enhance the motivation to read more whilst improving the proficiency of the English language.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.245
Teacher spread0.241 · 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 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
Published2014
Admission routes1
Has abstractyes

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