ESP in Vocational Institutes: A Mixed-Method Study of Students’ ESP Learning Style Preferences
Bibliographic record
Abstract
In vocational institutes, English for Specific Purposes (ESP) education should incorporate basic linguistic skills with professional communication abilities. There is a problem with ESP teaching that uses the traditional teaching model of general English, which fails to consider the needs of students. To tackle this problem, the aim of this study is to identify the preferred perceptual ESP learning styles of 254 students in seven Chinese vocational institutes. Data collection and analysis were conducted using a mixed-methods approach that combined both quantitative and qualitative methods. An adapted version of the Perceptual Learning Style Preference Questionnaire (PLSPQ) developed by Joy Reid (1987) was used at the first stage. Subsequently, in response to the questionnaire results, 15 students participated in semi-structured interviews. The results showed that a significant number of students chose minor learning modes rather than major learning modes. According to the data analysis, kinesthetic learning was the most preferred learning style, whereas group learning was the least preferred. The second to fifth places belonged to individual, tactile, visual, and auditory learning styles. The findings of the study have implications for ESP teachers, curriculum designers, and researchers considering students’ preferred learning styles, changes in the learning environment, and materials adaptations.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".