English-Medium Instruction in Higher Education in Uzbekistan: Views on Effectiveness, Career Prospects and Challenges
Bibliographic record
Abstract
Several higher educational institutions (HEI) have launched English-Medium programs in order to recruit foreign students and increase graduate qualification for better employability. The present study reports on the results of an online survey and semi-structured interviews of administrators, instructors and students towards the use of English as a medium of instruction in tertiary level in Uzbekistan, where Uzbek is the native language of the students and the pedagogical staff. The sample of the study consisted of 34 participants: 13 instructors, 15 students and 6 administrators at the universities of Uzbekistan. The results of the questionnaire and interviews show that implementing an EMI approach would attribute responsibility on the teaching staff and require very carefully designed curricula. The findings also suggest that EMI courses improve English proficiency, contribute to instructors’ professional development, career, and income and enhance students’ interaction with their international fellows. Such recommendations as motivating students, making classes more interesting and comfortable are made by both instructors and students pointing to the fact that English proficiency is crucial to the learning success of professional knowledge. EMI courses offer great opportunities to both students and instructors.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".