Are Prospective EFL Teachers Culturalist or Interculturalist?
Bibliographic record
Abstract
The research aimed to investigate the current cultural stance and the attitudes of prospective EFL teachers towards culture teaching. For this purpose, 200 teacher candidates (73 males 127 females) studying at a teacher training program of a state university were involved in the study. A demographic information form and two questionnaires were used to collect the data. The participants’ cultural intelligence profile was assessed under four sub-dimensions. Statistical analyses such as descriptive statistics, independent samples T-tests, Pearson Correlations, and ANOVA were used in analyzing the quantitative data. According to the results of the study, EFL student teachers had positive attitudes towards teaching culture in foreign language classes. They were also seen to have varying degrees of cultural intelligence. As for the effect of gender, age, and the year of the study at the faculty, the analyses revealed that gender and age were not related significantly to the attitudes towards culture teaching and cultural intelligence. The year of the study at the faculty seemed to have a significant relationship with the attitudes towards culture teaching and cultural intelligence. The last two years of undergraduate study at the ELT departments were seen to be significant on prospective EFL teachers’ culturalist or interculturalist stances. Finally, the researchers discovered a positive relationship between prospective teachers’ attitudes towards culture teaching and their meta-cognitive, motivational, and behavioral dimensions of cultural intelligence. Some recommendations were presented for future researchers and practitioners relying on the research findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".