Bridging the gap: a study of academic language-learning needs of Saudi international students
Bibliographic record
Abstract
Using quantitative and qualitative research methodologies, the current study examines Saudi students’ perspectives, coupled with EAL instructors’ views, regarding Saudi students’ English language-learning needs. Two data collection tools were used, a questionnaire and semi-structured interviews. The study (N = 172) included samples of EAL learners (n = 127) and EAL instructors (n = 45) both in Saudi Arabia and Canada, Victoria. The mismatch between the skills identified as important and areas identified as needing support by the learners, in addition to the divergence between learners’ and instructors’ perceptions, underscores the necessity of triangulation when using needs analysis to discover language-learning needs. The study delineates oral communication (i.e., being able to interact by using the language appropriately and efficiently) as a language-learning need identified by both Saudi students and their language instructors. Quantitative (skill ratings) and qualitative (responses to open-ended questions and interviews) data suggest that both students and instructors view writing as a challenging area for Saudi English-language learners. The results also indicate issues that contribute to the challenges faced by Saudis in the process of learning English. Findings show the importance of educational background and cultural differences in the students’ language development. Responses report that reading is devalued in the Saudi educational system and Saudi culture in general. Hence, both Saudi students and instructors in Canada pinpointed reading as an area needing support. Based on the key findings, it is evident that the language-learning needs of Saudi students are shaped according to the requirements of their immediate study context and their prospective goals. The study contributes crucial findings about participants’ perceptions of the importance of skills and their assessment of skills status in Saudi Arabia and Canada. In addition to the implications for English language learning in Saudi Arabia, these findings can be informative for educational institutions and practitioners in the English-speaking world. Most importantly, the multi-level analysis confirms that language learning needs are context-specific.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".