MétaCan
Menu
Back to cohort
Record W3155146424 · doi:10.5539/elt.v14n5p13

Japanese University Students’ Perceptions of Foreign English Teachers

2021· article· en· W3155146424 on OpenAlexvenueno aff
Soyhan Egitim, Travis Garcia

Bibliographic record

VenueEnglish Language Teaching · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMultimethodologyFocus groupPerceptionCultural competenceIntercultural communicationHigher educationPedagogyExploratory researchMathematics educationSociology

Abstract

fetched live from OpenAlex

This study aims to understand Japanese university students’ perceptions of foreign English teachers (FETs) through a two-phased exploratory sequential mixed-methods design. During the initial phase, a quantitative survey was performed with first and second-year Japanese university students (n=377). Despite a lack of precision and a high dispersion measure, the Quantitative data analysis revealed certain outliers. A significant number of participants viewed their FETs as more of an entertainer, preferred FETs of American/European heritage, and believed FETs of Asian descent may not be able to teach the language and culture as effectively as FETs of American/European heritage. Thus, a qualitative inquiry was performed to explain and build upon the quantitative findings. Two focus groups with students from the quantitative survey were given interviews. The responses confirmed the existence of phenotypical, gender, and personality FET stereotypes in Japanese university EFL classes. In addition, past educational experiences, socio-cultural factors, and mass media were also found influential in students’ perceptions of FETs. Based on the findings from the focus group interviews, the researchers propose intercultural activities as an effective pedagogical strategy to promote reflective teaching practices and intercultural competence in Japanese university EFL classes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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.

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

Citations15
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicEFL/ESL Teaching and LearningFrench-language works237,207