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Record W2152592372 · doi:10.5539/ies.v6n3p15

Teaching Immigrants Norwegian Culture to Support Their Language Learning

2013· article· en· W2152592372 on OpenAlexvenueno aff
Awal Mohammed Alhassan, Ahmed Bawa Kuyini

Bibliographic record

VenueInternational Education Studies · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianImmigrationPsychologyPedagogyMathematics educationPerceptionClass (philosophy)Qualitative researchSociologySocial scienceLinguisticsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This study was conducted with 48 adult immigrant students studying Norwegian under basic education program of the Ski Municipality Adult Education Unit between 2009-2011. Using the framework of Genc and Bada (2005), we tried to replicate their study in new setting –Norway. The study investigated migrant students’ perceptions learning Norwegian culture and its effects on their learning of the Norwegian language. The participants responded to a set of questionnaire and one open-ended question adapted from Bada (2000) and Genc and Bada (2005). Descriptive statistics and qualitative analysis procedures were used to analyse the data. The results showed that teaching culture to immigrant students raised their cultural awareness of their own and the Norwegian society. It also improved both their language skills and attitudes to the Norwegian culture. The study revealed some similarity between the students’ views and the theoretical benefits of a culture class as argued by some experts in the field. The results provide some further evidence of the benefits of learning cultural content as an integral part of learning a new 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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.003
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 head0.042
GPT teacher head0.350
Teacher spread0.308 · 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 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
Published2013
Admission routes1
Has abstractyes

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