Need Analysis: English Language Use by Students in the Tourism and Hospitality and Industry
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».