Strategies and Difficulties of Understanding English Idioms: A Case Study of Saudi University EFL Students
Bibliographic record
Abstract
This study aims to investigate difficulties face Saudi EFL students in learning and understanding English idioms, and examines the strategies they utilize to understand idioms. The subjects were 85 male and female Saudi English major university students at the Department of English in Aljouf University. Two data collection instruments, questionnaire, semi-structured interview were employed as well as the Nation’s Vocabulary Level Test to measure the students’ language proficiency level. The results showed that students have difficulty to understand idiomatic expressions. Moreover, the findings revealed that most frequently used strategies were guessing the meaning of idioms from context, predicting the meaning of idioms, and figuring out an idiom from an equivalent one in their mother language. Furthermore, the results illustrated that low-proficiency students face more difficulties than high-proficiency students, though the differences were not significant. The results also showed that, the greater the vocabulary knowledge, the greater the use of idiom-learning strategies, especially for idioms that require a wider knowledge in vocabulary. This study concludes with teaching implications and recommendation for further research in learning and understanding idiomatic expressions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".