The Development of English Lessons for Work by Using the Conversation Focusing on Practical English Vocabulary in Hospitality Industry for Thai EFL Students
Bibliographic record
Abstract
The study investigated the use of English lessons for work by using the conversation focusing on practical English vocabulary in the hospitality industry that has any effect on the students’ ability in using English conversation and vocabulary in the hospitality industry. Also, the present research explored the students’ opinions toward the materials. The objects consisted of 34 Non-English major students of undergraduate of Nakhon Pathom Rajabhat University. The researcher used the paired sample t-test to analyze the students’ ability in using English conversation and vocabulary in the hospitality industry before and after using the materials. Besides, the mean and standard deviation of items were used to evaluate the students’ opinions toward the materials constructed. The results revealed that the efficiency score of the materials constructed was higher than the excepted criterion. Also, the students’ ability in using English conversation and vocabulary in hospitality industry materials constructed at the 0.05 level, and the students’ opinions toward the materials constructed were generally high.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".