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Record W2780811256 · doi:10.5430/wjel.v7n4p18

Identity, Motivation and English Learning in a Japanese Context

2017· article· en· W2780811256 on OpenAlexvenueno aff
Nooshin Goharimehr

Bibliographic record

VenueWorld Journal of English Language · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyIdentity (music)Context (archaeology)Intrinsic motivationSocial psychologyMotivation to learnGoal theoryMathematics educationPedagogy

Abstract

fetched live from OpenAlex

Founded upon motivation, identity and self theories, this qualitative case study explored the motivational self systemand identities of Japanese EFL learners and their influence on motivation and English language learning. Data wascollected through online surveys among 22 graduate and undergraduate university students. The survey resultsindicated high motivation, international orientation and positive attitudes toward English language learning. Thethematic analysis of students’ detailed responses to the open-ended questions showed a stronger instrumentalmotivation and lack of desire to join and identify with the English communities and culture. International orientationappeared to be a better measure of motivation as opposed to integrative motivation. Moreover, the learners hadinhibitory factors operating against English learning motivation and speaking practices such as anxiety and lowlinguistic self-confidence. Resistance to new cultural identities or identity conflicts resulted from different culturalcontexts show to be an influencing factor in L2 learning. In sum, combining Gardner’s views on motivation,Norton’s conceptions of identity and Dörnyei's L2 Motivational Self System together with qualitative approachesmight render a deeper understanding of motivational barriers of Japanese EFL learners.

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)0.006
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 score0.359
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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