Need Analysis: English Language Use by Students in the Tourism and Hospitality and Industry
Bibliographic record
Abstract
This research had 3 objectives. First, to discover the main skills students studying in Tourism (TR) and Hospitality Industry (HI) need to successfully use English. Second, to ascertain the essential skills that students need in English language acquisition. Last, to investigate which skills students found to be the most problematic in English language learning. The informants of 160 students were split by the quota sampling method into two equally sized groups of 80 learners each; i.e., 80 were placed into the Tourism group and 80 into the Hospitably group. Twenty participants were selected equally from each academic year - from first year students to fourth year students - for placement into each category of TR and HI. To acquire relevant data a learner’s perception questionnaire was employed as well as interview questions. Average and standard deviation were used to examine the questionnaire data and content analysis for the interview data. There was a .05 difference statistically between the hospitality industry and tourism students’ English scores. The hospitality industry students scored slightly higher. Conversely, when comparing the different levels of students' attitudes in the 5 language skills, the tourism students scored higher than the hospitality industry students, yet the difference of .05 was also not statistically significant. Additionally, the outcome from a comparison of student satisfaction levels toward English teaching and learning, found that tourism students reported higher satisfaction levels than those of the hospitality industry students. However, the .05 degree of difference was not statistically significant. Also, neither of the student groups were significantly different in terms of enhancing their English skills. Additionally, the interview results showed that improving English language skills and grammatical structures were the skills the students' reported needing the most assistance with in their studies. Neither of the student groups were significantly different in terms of enhancing their English skills. The study found that the main reason that students of both majors desired to improve their English language skills was to improve their grades. The obstacles faced by the students in these two sample groups in using the English language are not very different, since most of the interviewed people reported that the skills of listening and speaking as being the greatest problems in their communication. Another similarity between the two sample groups was that students of both majors expressed a positive attitude toward their English language learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".