The Library’s Impact on University Students’ Academic Success and Learning
Bibliographic record
Abstract
Abstract Objective – The purpose of this study was to examine relationships among student library visits, library resource use, library space satisfaction (e.g., quiet study space), and students’ academic performance (i.e., Grade Point Average or GPA) using quantitative data and to better understand how the academic library has an impact on students’ learning from students’ perspectives using qualitative data. Methods – A survey was distributed during the Spring 2018 semester to graduate and undergraduate students at a large public research institution. Survey responses consisted of two types of data: (1) quantitative data pertaining to multiple choice questions related to the student library experience, and (2) qualitative data, including open-ended questions, regarding students’ perceptions of the library’s impact on their learning. Quantitative data was analyzed using Spearman’s rank correlations between students’ library experience and their GPAs, whereas qualitative data was analyzed employing thematic analysis. Results – The key findings from the quantitative data show that student library visits and library space satisfaction were negatively associated with their GPA, whereas most students’ use of library resources (e.g., journal articles and databases) was positively associated with their GPAs. The primary findings from the qualitative data reveal that students perceived the library as a place where they can concentrate and complete their work. Additionally, the students reported that they utilize both the quiet and collaborative study spaces interchangeably depending on their academic needs, and expressed that the library provides them with invaluable resources that enhance their coursework and research. Conclusions – While the findings show that the student library experience was associated with their academic achievements, there were mixed findings in the study. The findings suggest that as a student’s GPA increases, their in-person library visits and library space satisfaction decrease. On the other hand, as a student’s GPA increases, their library resource usage increases. Further investigation is needed to better understand the negative relationship between students’ library visits, library space satisfaction, and their GPAs.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".