University Students’ Changing Library Needs and Use: A Comparison of 2016 and 2018 Student Surveys
Bibliographic record
Abstract
Abstract Objective – This study examines differences in university students’ library use and satisfaction (e.g., in-person library visits, online and print resource use, space satisfaction, and library website use) between 2016 and 2018 based on local survey data. It also discusses how these findings provided guidance for future planning and action. Methods – The academic university library developed the surveys for undergraduate and graduate students and distributed them in Spring 2016 and 2018. Both student surveys focused on examining students’ needs relative to library resources and services, although the 2018 student survey also attempted to quantify students’ library visits and their use of library resources. While the surveys were not identical, the four questions that appeared in both surveys (i.e., library visits, resource use, library space satisfaction, and library website use) were recoded, rescaled, and analyzed to measure the differences in both surveys. Results – The survey results reveal that students’ library visits and use of library resources in 2018 were higher than in 2016. In particular, undergraduate students’ use of library resources in 2016 were lower than those in 2018, whereas graduate students’ use of library resources remained similar in both years. Another key finding indicates that the mean score of students’ library quiet study space satisfaction in 2018 was higher than in 2016. However, when compared to the 2016 survey, there was a decrease in students’ ease of library website use in the 2018 survey. Conclusion – Assessing students’ behavior and satisfaction associated with their use of library physical spaces, resources, and services should be conducted on an ongoing basis. Over time, the survey findings can be used as evidence based data to communicate patterns of users’ behavior and satisfaction with various stakeholders, identify areas for improvement or engagement, and demonstrate the library’s impact. Survey results can also inform further strategic and assessment planning.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".