The Englisg Proficiency of Civil Engineering Students at a Malaysian Polytechnic
Bibliographic record
Abstract
The purpose of this study was to investigate the English proficiency of civil engineering students of a Malaysian polytechnic. A questionnaire, modeled after the Programme for International Student Assessment (PISA) approach and The Secretary’s Commission on Achieving Necessary Skills report was developed and administered to 171 civil engineering students. These students had completed a mandatory one-semester industrial training programme with various organizations. This post industrial training survey, through the use of a self-report questionnaire, provided an important opportunity to capture crucial data from students regarding their English language skills. Findings of this study revealed that the students frequency or ability of using the English language was low, irrespective of the type of workplace or level of study. Analyses of skill deficiencies revealed wide learning gaps between the acquired and required English skill attributes. Analysis of the survey data had also identified a list of important skill attributes in the workplace, and the four most highly valued English skill attributes were a combination of academic and specific job-related tasks: understanding technical documents, correct grammar, vocabulary and sentence structure, writing test/investigation report and questioning for clarification. The results of this study implied the need for curriculum changes (such as content and mode of delivery) so that polytechnic graduates could meet the workplace expectations. Key words : Employability Skills, English Proficiency, Skills Gaps
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".