LIS Students at a Japanese University Use Smartphones for Social Communication more often than for Educational Purposes
Why this work is in the frame
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Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.282 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it