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Record W4288077513 · doi:10.3389/fpsyg.2022.939516

Teacher cognition in teaching intercultural communicative competence: A qualitative study on preservice Chinese language teachers in Hong Kong SAR, China

2022· article· en· W4288077513 on OpenAlexaff
Yang Gong, Chun Lai, Xuesong Gao, Guofang Li, Yingxue Huang, Lin Lin

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of British Columbia
FundersUniversidade de Macau
KeywordsCommunicative competencePsychologyCognitionChinaCompetence (human resources)Intercultural competenceIntercultural communicationCommunicative language teachingPedagogyLanguage educationMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study is to examine preservice Chinese language teachers' cognition in teaching intercultural communicative competence. In the study we collected data through in-depth interviews with seven preservice teachers in a Master of Education program (Teaching Chinese as a Second Language, TCSL) at a university in Hong Kong SAR, China. The findings indicated that the participants had a relatively positive attitude and inclination toward the development of students' intercultural communicative competence, while their conceptualizations of culture tended to be static and ambiguous. In addition, the participants' objectives in teaching intercultural communicative competence were found to be more attitude-than knowledge- or skill-oriented. The study offers valuable insights that preservice language teachers' cognition plays a crucial role in their future professional development and calls for curricular innovations with intercultural aims in teacher education programs.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.084
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.037
GPT teacher head0.380
Teacher spread0.343 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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