Use of smartphones by art and design students for accessing library services and learning
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore art and design students’ use of smartphones for accessing library services and learning at the Hong Kong Design Institute (HKDI). Design/methodology/approach – A questionnaire survey involving 51 HKDI students was conducted to examine the students’ utilization of apps and the internet on mobile devices to find information for the purpose of academic learning, social networking, and collaborative learning. Findings – Survey results showed that while the HKDI students were all smartphone owners and active users of such mobile communication devices, only a minority of them “frequently” use these mobile devices for formal learning purposes. They demonstrated a keen preference to use search engines, social communications, and other diverse use of smartphones. Except for research and image/audio-visual needs, the rest of their needs and usage behaviour is similar to mainstream university students. Practical implications – The results suggest opportunities for the libraries to develop services and facilities that could better fulfil students’ information needs, and to improve the network coverage outside the library. Originality/value – This is probably the first study of its kind to explore art and design students’ use of smartphones for learning needs. In particular, the recent capability of smartphones and mobile internet speed are comparable with desktops, it is vital to re-examine the much changed environment and user needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".