English-Medium Instruction and Content Learning in Freshman Year: An Investigation of a Saudi University Students’ Challenges and Learning Strategies
Bibliographic record
Abstract
The present study investigates the challenges that science freshmen perceive in English-medium instruction at Prince Sattam bin Abdelaziz University in terms of language and learning as well as the frequency of relevant learning strategies employed by students. A questionnaire was used to collect data from 376 students enrolled in the First Year Program at Prince Sattam Bin Abdelaziz University, considering their gender, scientific tracks, and previous English exposure. Results reveals that females were less comfortable communicating with professionals in their classrooms. Simultaneously, freshmen females perceive greater challenges in content comprehension, knowledge application, and learning adaptability. Therefore, they relied on learning strategies supported by L1 more frequently than males. Comparison of groups based on tracks shows that engineering students have more difficulty communicating with professionals than medical students. Furthermore, freshmen with extensive prior English exposure had fewer difficulties communicating with their peers and professionals. They perceived fewer difficulties with content comprehension, knowledge application, and learning adaptability. In contrast, freshmen with little prior exposure to English relied more on L1-related learning strategies. The findings show significant differences in perceiving EMI-related challenges and adopted learning strategies based on the relevant variables. They suggest that the shift from high school Arabic-medium education to English-medium instruction in higher education requires careful institutional and individual planning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".