Arabic-Speaking EFL Learners’ Recognition, and Use of English Phrasal Verbs in Listening and Writing
Bibliographic record
Abstract
This study investigates the usefulness of acquiring English PVs (as a key component of English vocabulary) using listening activities. Therefore, this study analyzes how Arabic speakers studying English as a foreign language (EFL) understand and use English phrasal verbs through listening. A self-administered survey was distributed to 74 students, mainly from Saudi Arabia. They listened to a recording incorporating frequently used English phrasal verbs and identified those they could recognize. The survey also measured the ability of respondents to provide sentences in which they used phrasal verbs and gave their meanings in Arabic. The findings indicated that EFL students are likely more familiar with phrasal verbs in writing than in an oral context. For instance, the average respondent could detect six or seven out of ten phrasal verbs they heard, while about 90% of respondents could use the provided phrasal verbs correctly in writing. Respondents recognized some phrasal verbs more than others. At least 80% recognized “pick me up,” “go on,” and “go out,” while less than 47% recognized “came out” and “set up.” More than 81% knew the correct definition of phrasal verbs. These findings offer foundational data to help improve methodologies for Arabic speakers learning EFL through listening activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".