Educational Environment and the Quality of Foreign Language Teaching: Perspectives of Kazakhstani Urban and Rural EFL Teachers
Bibliographic record
Abstract
The disparity in the quality of foreign language education between urban and rural secondary schools in Kazakhstan has become increasingly prominent in recent years. Despite the national goal of achieving B2-level proficiency, students in rural areas often encounter obstacles that impede their language learning progress. These challenges stem from factors such as limited access to qualified teachers, inadequate resources, socioeconomic disadvantages, and geographic isolation. Although previous researchers have recognized these factors, there remains a gap in understanding the perspectives of English as a foreign language (EFL) teachers regarding the quality of language education. To fill this gap, this study’s authors aim to explore the perspectives of EFL teachers in both urban and rural schools. Employing a mixed-method research design, the researchers integrate quantitative and qualitative approaches through surveys and semi-structured interviews. They administer a quantitative survey online with 524 participants, comprising 313 urban teachers and 211 rural teachers. Additionally, they conduct 20 individual semi-structured interviews with EFL teachers from secondary schools in Kazakhstan. The authors investigate six key components of the language educational environment that influence EFL teaching quality: 1) linguistic (teachers’ language proficiency), 2) sociopsychological (teacher–student interaction), 3) methodical (teachers’ professional development), 4) information and communication (ICT knowledge), 5) intercultural (teachers’ intercultural competence), and 6) managerial (teachers’ involvement in school administration). By conducting qualitative analysis of interviews and quantitative analysis of survey data, the researchers elaborate on characteristics of these components of the educational environment that may underlie the observed disparities in language learning outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".