The Adequacy of Credit English Training Hours in Technical Colleges: Perspectives of Trainers and Trainees
Bibliographic record
Abstract
Adequate training time in technical and vocational education and training institutions is critical for effective language acquisition. This study aimed to investigate the perceptions of TVET’s trainers and trainees regarding the adequacy of English language training hours in fostering students’ personal and professional success. The 8-week study employed a quantitative approach, collecting data via structured surveys with Likert-scale questions from 60 trainers and 147 trainees. Results were computed using descriptive statistics, and an independent t test was used to identify statistically significant differences between trainer and trainee responses. The findings showed that trainers expressed neutrality toward the adequacy of training hours, with concerns about insufficient time for teaching language skills, particularly speaking and writing. Trainees were more positive, although they also identified challenges in listening and reading. A statistically significant difference was observed between trainers’ and trainees’ perceptions (p = 0.001), with trainees viewing the current hours more favorably. Both groups agreed on the need for increased training hours. Trainers emphasized the importance of additional time for improving instructional methods and supporting learners facing challenges, while trainees believed that increased hours would lead to better language proficiency and job readiness. The study underscores the critical role of sufficient training hours in enhancing language skills necessary for personal and professional success, thus providing a foundation for future policy adjustments and educational improvements in vocational training contexts.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".