Impact of an ESP Course on English Language Proficiency of Undergraduate Engineering Students: A Case Study at Dhofar University
Bibliographic record
Abstract
Over the last several decades, graduate students from engineering courses have faced a challenge of regular rejections in the work market despite their outstanding academic qualifications. In response to this challenge, many universities across the globe have introduced in their curricula the English for Specific Purposes (ESP) courses tailored to the need of engineering students. The present study evaluated the effectiveness of the ESP course for engineering students introduced at Dhofar University in Oman. The study participants were first- and second-year undergraduates from the Faculty of Engineering. The participants responded to a 26-item survey that addressed the course content and the changes in the students’ English language proficiency. The results demonstrated that taking the ESP course had a positive impact on the course content, participants’ vocabulary and grammar, as well as on their specific English language skills. The limitations of the study include a relatively small sample of participants and the self-reporting bias inherent in the use of a self-report methodology. Therefore, further research using more objective measures to evaluate the effectiveness and impact of ESP courses on English proficiency of engineering students would be needed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".