Media Use Among Students From Different Health Curricula: Survey Study
Notice bibliographique
Résumé
BACKGROUND: Mobile devices such as smartphones, tablets, and laptop computers enable users to search for information and communicate with others at any place and any time. Such devices are increasingly being used at universities for teaching and learning. The use of mobile devices by students depends, among others, on the individual media literacy level and the curricular framework. OBJECTIVE: The objective of this study was to explore whether there were differences in media use in students from various curricula at the Faculty of Health, Witten/Herdecke University. METHODS: During the 2015-16 winter term, a survey was conducted at the Faculty of Health, Witten/Herdecke University, in which a total of 705 students (out of 1091 students; response rate: 705/1091, 64.61%) from 4 schools participated voluntarily: medicine (346/598), dentistry (171/204), psychology (142/243), and nursing science (46/46). The questionnaire developed for the study included 132 questions on 4 topics: (1) electronic and mobile devices (19 questions), (2) communication and organization of learning (45 questions), (3) apps/programs/websites/media (34 questions), and (4) media literacy (34 questions). The questionnaire was distributed and anonymously completed during in-class courses. RESULTS: Students from all 4 schools had at least two electronic devices, with smartphones (97.4%, 687/705) and laptops (94.8%, 669/705) being the most common ones. Students agreed that electronic devices enabled them to effectively structure the learning process (mean 3.16, SD 0.62) and shared the opinion that university teaching should include imparting media literacy (mean 2.84, SD 0.84). Electronic device ownership was the highest among medical students (mean 2.68, SD 0.86) and medical students were the only ones to use a tutorial (36.1%, 125/346). Dental students most widely used text messages (mean 3.41, SD 0.49) and social media (mean 2.57, SD 1.10) to organize learning. Psychology students considered mobile devices to be most ineffective (mean 2.81, SD 0.83). Nursing science students used emails (mean 3.47, SD 0.73) and desktop computers (39%, 18/46) most widely. CONCLUSIONS: The results show that almost all students use electronic learning (e-learning) tools. At the same time, different profiles for different degree programs become apparent, which are to be attributed to not only the varying curricula and courses but also to the life circumstances of different age groups. Universities should, therefore, pay attention to the diverse user patterns and media literacy levels of students when planning courses to enable successful use of e-learning methods.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 tête enseignante, 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 ».