LIS Students at a Japanese University Use Smartphones for Social Communication more often than for Educational Purposes
Notice bibliographique
Résumé
A Review of: Lau, K. P., Chiu, D. K. W., Ho, K. K. W., Lo, P., & See-To, E. W. K. (2017). Educational usage of mobile devices: Differences between postgraduate and undergraduate students. The Journal of Academic Librarianship, 43(3), 201-208. https://doi.org/10.1016/j.acalib.2017.03.004 Abstract Objective – To discover how undergraduate (UG) and graduate (G; “postgraduate” [PG] in the original article) students of library and information science (LIS) use mobile devices and to understand preferences and perceived barriers to educational use. Design – Survey questionnaire. Setting – University in Japan. Subjects – Ninety undergraduate students (30 male, 60 female) and 30 graduate students (13 male, 17 female). Nineteen additional recruits were excluded from the study due to incomplete surveys. Almost all subjects (>98%) were born between 1982 and 2002. Methods – Subjects were recruited without incentives from one LIS department. An online survey was conducted with the purpose of gathering information on how often devices were used for various activities, perceived barriers to mobile learning (m-learning), and demographic data. The survey was modeled on a 2015 study of LIS students in Hong Kong, Japan, and Taiwan (Ko, Chiu, Lo, & Ho, 2015). The Mann-Whitley U test was used to investigate possible significant differences between UG and G responses. Main Results – 94.2% of participants had smartphones with Internet access; both UG and G subjects reported weekly to daily use for social communications (email, short message service [SMS], chat, and social media) and for querying search engines. Both UG and G subjects reported using finance and banking services less than once a month. Other activities (shopping, finding locations, entertainment, sports, tools and productivity software, casual reading, academic reading, accessing reference materials, accessing libraries) for both groups fell within the range of less than once per month to weekly use. Unlike G subjects, UG subjects reported significant (p < 0.05) engagement with social media and marginal (p < 0.10) engagement with accessing libraries, and productivity tools. In terms of educational use, neither UG nor G subjects reported daily m-learning behaviors, instead reporting monthly to weekly browsing of online information and social networking sites, with far less (i.e., less than once a month) engagement with professional articles, e-books, learning management platforms, and several other activities (listening to podcasts, viewing videos, “other”). UG subjects reported significant marginal (p < 0.10) engagement with “other” materials, unlike G subjects. Library catalogs and databases were less likely to be used when compared to reference sources, with UG and G subjects reporting monthly or less use for these. When asked if they would use mobile library services, respondents answered “maybe interested if available”, with UG subject reporting significant marginal (p < 0.10) engagement vs. G subjects for several of these services. Regarding productivity activities, both UG and G subjects reported monthly or less use of note taking, word processing, and scheduling tools. For communication and sharing activities, subjects reported monthly or less activity for communicating with classmates, using email for study-related issues, posting to discussions on learning management platforms, posting or commenting about their studies on social networking sites, sending photos or videos to social media, moving document files, and scanning Quick Response (QR) codes. UG subjects were marginally (p < 0.10) more engaged in communicating with classmates than G subjects. Barriers to m-learning were not considered “high” barriers, with “low” to “medium” barriers for both UG and G subjects being small screen size, non-mobile format, difficulty typing, challenges with authentication, no Wi-Fi, difficulty reading, lack of specialized apps, and slow loading times. Conclusion – This study provides a snapshot of how participants used mobile devices at the time the survey was conducted. Both UG and G subjects used their devices for social communication more than for educational purposes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
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 ».