Perceptions and Problems of English Language and Communication Abilities: A Final Check on Thai Engineering Undergraduates
Bibliographic record
Abstract
English language and communication abilities are an essential part of the global engineering community. However, non-native English speaking engineers and students tend to be unable to master these skills. This study aims to gauge the perceived levels of their general English language proficiency, to explore their English communicative problems, to investigate their perceived abilities when performing English-related tasks in an engineering workplace communication situation, and to obtain feedback on student performances from English instructors in English for Specific Purposes (ESP) courses. The participants included 130 Thai undergraduate students and two English instructors at a government university. There were two instruments; a questionnaire for the students and a series of interview questions for the instructors. The results revealed that (a) although the students perceived their abilities to be at a fair level, they experienced difficulty using productive skills in English communication; (b) the English-related tasks that the students performed best and worst in were reading and writing tasks respectively; and (c) in the ESP courses, the ability of the students to use English in the ‘real world’ was not dramatically improved, and (d) these students also had unrealistic language learning goals. These results would benefit both ESP instructors and stakeholders in terms of increasing awareness of both language and communication problems, and designing tailor-made courses that are a perfect fit for their students with regard to the contemporary engineering community.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".