Hong Kong Students Consider Virtual Reference a Vital Service and It Can Aid in Many Stages of Learning
Bibliographic record
Abstract
A Review of: Tsang, A. L. Y., & Chiu, D. K. W. (2022). Effectiveness of virtual reference services in academic libraries: A qualitative study based on the 5E learning model. The Journal of Academic Librarianship, 48(4), Article 102533. https://doi.org/10.1016/j.acalib.2022.102533 Objective – Understand how virtual reference services (VRS) impact students’ learning using the 5E model (engage, explore, explain, elaborate, evaluate) as a theoretical framework. Design – Exploratory qualitative study. Setting – Major university in Hong Kong. Subjects – There were 10 participants between the ages of 18 and 35, including undergraduate and postgraduate students and one alumnus of the university. Methods – Online synchronous semi-structured interviews of 30 minutes via Zoom. Interview data were transcribed and analyzed thematically according to the 5E learning model. Main Results – WhatsApp was the preferred form of VRS, over Zoom, email, or phone. VRS can facilitate better awareness of library resources and supports resource exploration. WhatsApp VRS is particularly valuable for students who may find other modes intimidating, overly formal, or inaccessible due to time constraints. VRS has grown in importance since the COVID-19 pandemic. Conclusion – VRS provided via instant messaging is a valued service for students, but libraries, library websites, and librarians can all work to improve awareness of the option and possible uses. Future work is needed to understand how demographics may influence patrons’ attitudes and experiences of VRS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".