Understanding Patterns of Library Use Among Undergraduate Students from Different Disciplines
Bibliographic record
Abstract
Abstract Objective – To test whether routinely-generated library usage data could be linked with information about students to understand patterns of library use among students from different disciplines at the University of Huddersfield. This information is important for librarians seeking to demonstrate the value of the library, and to ensure that they are providing services which meet user needs. The study seeks to join two strands of library user research which until now have been kept rather separate – an interest in disciplinary differences in usage, and a methodology which involves large-scale routinely-generated data. Methods – The study uses anonymized data about individual students derived from two sources: routinely-generated data on various dimensions of physical and electronic library resource usage, and information from the student registry on the course studied by each student. Courses were aggregated at a subject and then disciplinary level. Kruskal-Wallis and Mann Whitney tests were used to identify statistically significant differences between the high-level disciplinary groups, and within each disciplinary group at the subject level. Results – The study identifies a number of statistically significant differences on various dimensions of usage between both high-level disciplinary groupings and lower subject-level groupings. In some cases, differences are not the same as those observed in earlier studies, reflecting distinctive usage patterns and differences in the way that disciplines or subjects are defined and organised. While music students at Huddersfield are heavy library users within the arts subject-level grouping arts students use library resources less than those in social science disciplines, contradicting findings from studies at other institutions, Computing and engineering students were relatively similar, although computing students were more likely to download PDFs, and engineering students were more likely to use the physical library. Conclusion – The technique introduced in this study represents an effective way of understanding distinctive usage patterns at an individual institution. There may be potential to aggregate findings across several institutions to help universities benchmark their own performance and usage; this would require a degree of collaboration and standardisation. This study found that students in certain disciplines at Huddersfield use the library in different ways to students in those same disciplines at other institutions. Further investigation is needed to understand exactly why these differences exist, but some hypotheses are offered.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".