Language-Related Barriers and Insights to Overcome the Challenges of English Medium Instructed Learning Environment for Undergraduates
Bibliographic record
Abstract
The practice of English Medium Instruction in the tertiary phase of education in the non-Anglophone circle is a significant but perplexing argument. The void of academic exploration of the students’ authentic perspectives and the challenges they face due to the quick transfer of the academic language from L1 to L2 without any smooth procedure is a critical ground that needs investigation. Thus, the current study aimed to explore the language-caused challenges and the strategies utilized by the students to overcome the challenges of the EMI learning environment in the tertiary phase of education. The study utilized the purposive sampling method and data was collected through a questionnaire survey and semi-structured interviews. Qualitative thematic analysis was utilized to analyze the collected data. The findings highlight that the significant language gap between secondary and tertiary education is the primary reason for students' language difficulties. However, students have developed strategies to tackle these language-related challenges. The study concludes by proposing potential solutions to facilitate a smoother transition from an L1-based learning environment to an L2-based learning environment.
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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 | 0.005 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".