Challenges of the Sudden Switch from Mother Tongue Instruction to English as a Medium of Instruction
Bibliographic record
Abstract
The present study explored the challenges encountered through the transition from using the mother tongue as a medium of instruction at schools to using English as a medium of instruction at universities. Two focus groups were conducted with Saudi undergraduates and faculty members from different Saudi universities. The focus groups investigated how participants perceive this experience, what difficulties they face and how they cope. Participants expressed their preference for using English as a medium of instruction in higher education to maximise students’ future and international opportunities. Participant students reported difficulties in lecture comprehension, taking notes while listening and classroom communication. Participant content lecturers reported difficulties related to students’ reluctance to speak in English, lack of English terminology and insufficient lecture comprehension. Some suggestions that have been offered to overcome these challenges include the following: designing adequate trainings for content lecturers on teaching their content in English; using Arabic-English bilingualism as medium of instruction; giving emphasis to academic literacy and communication skills over the use of standard English models and enhancing the collaborative work between English language teaching practitioners and content lecturers.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".