Exploring the Use and Challenges of Phrasal Verbs among EFL Arabic Medical Students
Bibliographic record
Abstract
This quantitative study investigates the factors that affect medical students from Saudi Arabia in their study of English as a foreign language. The study also explores the understanding and usage of frequent medical phrasal verbs among Arabic-speaking medical students. Through an online survey, data were gathered from 289 Saudi medical students studying English as a foreign language at various universities across Saudi Arabia. For data collection, the researcher developed a survey that measured the cognition of phrasal verbs and the students’ perceptions of the challenges they faced, the support their institutions provided, and the perception of their ability to use phrasal verbs. Findings revealed a strong positive correlation between support and proficiency (r = 0.578), whereas a negative correlation was found between challenges and proficiency (r = 0.193). This means that as support for learning English increased, perceived hurdles decreased. Findings also suggested that increased levels of support contributed to increased proficiency. Results indicated that years of studying English functioned as a moderator, improving spoken English. This study highlighted the significance of different strategies, including targeted and contextually relevant strategies, which improve the teaching of phrasal verbs to Arabic-speaking medical students. The study’s results focused on the need to utilize immersive, practical, and interactive methods in course syllabi to motivate students to use English in authentic contexts, such as reading medical documents, listening to medical discussions, and engaging in social networking platforms. These activities can significantly enhance the understanding and usage of phrasal verbs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".