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Record W2748518474 · doi:10.5430/ijhe.v6n4p229

The Relationship between Ideal L2 Self and Willingness to Communicate Inside the Classroom

2017· article· en· W2748518474 on OpenAlexvenueno aff
Nihan Bursalı, Hüseyin Öz

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsWillingness to communicateIdeal (ethics)Foreign languagePsychologyScale (ratio)Mathematics educationDescriptive statisticsCurriculumEnglish as a foreign languageSocial psychologyPedagogyMathematicsPolitical science

Abstract

fetched live from OpenAlex

Over the past decades there has been a dramatic increase in academic research on motivation to learn a second or foreign language (L2). The present study tried to investigate the relationship between the ideal L2 self as a motivational variable and willingness to communicate in English (L2 WTC) inside the classroom. Participants were 56 university students majoring in English as a foreign language (EFL) at a private university in Ankara, Turkey. Data were collected using the Ideal L2 Self Scale and Willingness to Communicate inside the Classroom Scale. Findings of descriptive statistics indicated that 32.1% of the participants had high L2 WTC inside the classroom, 30.4% had moderate L2 WTC inside the classroom, and 37.5% had low L2 WTC inside the classroom. Findings also revealed a significant relationship between these two constructs noticing the relations at a skills specific level. The implications are discussed to present ideas to language teachers, teacher trainers, and curriculum designers to raise their awareness on the impact of ideal L2 self on willingness to communicate.

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)0.008
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.088
GPT teacher head0.368
Teacher spread0.281 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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