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Record W2110145142 · doi:10.5539/elt.v8n3p193

A Comparative Study of Foreign Language Anxiety and Motivation of Academic- and Vocational-Track High School Students

2015· article· en· W2110145142 on OpenAlexvenueno aff
Hui-ju Liu, Chien-wei Chen

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAnxietyVocational educationForeign languageForeign language anxietyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

This study aimed to investigate EFL learner language anxiety and learning motivation of high school students. Subjects included 155 students from the same private senior high school in central Taiwan, 60 in academic track and 95 in vocational track. The majority of the participants started taking English lessons either before entering elementary school or during the first two years in elementary school. Statistical methods were conducted to investigate 1) whether learner motivation and language anxiety significantly vary between academic- and vocation-track high school students, 2) whether both academic- and vocational-track high school students feel an above-average level of language anxiety, and 3) whether there is a significant relationship between language anxiety and motivation among the EFL high school students. The findings of the study revealed that first, both groups of students felt moderate levels of language anxiety; there were no significant differences in anxiety level between the two groups of students. Second, students in the academic-track were also found to have higher extrinsic motivation and overall learning motivation than their vocational-track counterparts. Furthermore, a significant negative relationship was identified between the two important affective variables, motivation and anxiety. Important pedagogical implications for English teachers were discussed in the study.

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 score0.082
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.314
Teacher spread0.276 · 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

Citations49
Published2015
Admission routes1
Has abstractyes

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