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Record W3134768803 · doi:10.5539/ells.v11n1p38

Islamic Self-Identity Formation Through Language Learning: A Study of Religious Secondary School Students in Malaysia

2021· article· en· W3134768803 on OpenAlexvenueno aff
Mohd Nazri Latiff Azmi, Isyaku Hassan, Engku Muhammad Tajuddin Engku Ali, Ahmad Taufik Hidayah Abdullah, Mohd Hazli bin YahaAlias, Muzammir Anas, Nur Izzati Suhaimi

Bibliographic record

VenueEnglish Language and Literature Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
FundersUniversiti Sultan Zainal Abidin
KeywordsIslamIdentity (music)Mathematics educationPsychologyNonprobability samplingThematic analysisFocus groupPedagogyConstruct (python library)SociologyQualitative researchSocial sciencePopulationComputer scienceAnthropologyGeography

Abstract

fetched live from OpenAlex

Self-identity formation becomes increasingly challenging for students as they are exposed to different norms in the school environment. Education, language, and religion are crucial in the process of self-identity formation. Therefore, this study aims to explore how English language learning and the school environment influence Islamic self-identity formation among students in selected religious secondary schools in Terengganu, Malaysia. The study employed a qualitative approach in which 90 religious secondary school students in the State of Terengganu were selected using a purposive sampling technique. Focus group interviews were used as a data-gathering instrument. The students were divided across different sessions to ease the process of data collection. The data were transcribed and analyzed using inductive thematic analysis. The study found that English language learning does not influence the students’ Islamic self-identity formation negatively. Instead, certain morals such as respect, self-esteem, and cooperation, are instilled in the students’ self-identity. This study provides evidence on the students’ ability to construct Islamic self-identity despite the challenges of second language learning.

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)0.001
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 score0.051
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.329
Teacher spread0.321 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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