Don’t Make Me Type: A Study of Students’ Perceptions of Library Catalogues on Tablet Computers
Bibliographic record
Abstract
The objective of this mixed methods pilot study was to ascertain university students’ perceptions of online library catalogues using tablet computers, to determine how the participants used tablets and whether or not the NEOS consortium catalogue (NEOS) played an important role in the participants’ academic research. The researcher recruited four students from the University of Alberta who were each asked to use NEOS to complete a series of simple timed usability tasks on a tablet computer of their choosing. The participants also answered a variety of semi-structured interview questions regarding their tablet usage, internet browsing habits, device preferences, general impressions of NEOS, and whether they were receptive to the idea of a mobile NEOS application. Overall, the students found the functionality and design of NEOS to be adequate. Typing, authentication, and scrolling through lists presented consistent usability problems while on a tablet. Only one participant was receptive to the idea of a NEOS application, while the other three participants said tablets were not conducive to conducting academic research and that they preferred using a web interface on a laptop or desktop computer instead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".