Experienced teachers’ beliefs and practices toward communicative approaches in teaching English as a foreign language in rural Ukraine
Bibliographic record
Abstract
Communicative language teaching (CLT) in an English as a foreign language (EFL) context is a dynamic process involving teachers’ perspectives and practices. Although CLT is a widely accepted approach to second language instruction, scholars in the field continue to have a narrow understanding of how teachers conceptualize and implement this approach in various international contexts. The present multiple case study focuses on the interrelationships between in‐service teachers’ beliefs and practices with CLT in an EFL context: rural Ukraine. Ukraine only recently adopted a national CLT curriculum, and how experienced teachers integrate this approach into their current teaching has not been closely examined. To uncover mediating factors that influence their practices, the researchers drew on the approach of language ecology to analyze three experienced teachers’ beliefs and practices. Drawing on multiple data sources (surveys, interviews, and classroom observations), they identified two primary mitigating factors: access and privilege. The study's overall rich description in an asset for the EFL or English as a second language (ESL) professional, but the authors also integrated the findings into two reflective tools to guide teachers in self‐evaluation of their own communicative‐based teaching, their teaching situation, and supplementary training opportunities to enhance their teaching practices.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".