Linking Library to Student Retention: A Statistical Analysis
Bibliographic record
Abstract
Abstract Objective - This study analyses both library expenditure and student retention. It seeks to determine if positive correlations found in a former study endure using more recent data or if alternative interpretations can be made. It includes the associate degree-granting colleges and examines whether library instruction has a greater significance on student retention over expenditure and if library instruction at the two-year college correlates to retention. Methods - The colleges and universities included in the study grant associate, bachelor, masters, and doctoral degrees, based on Carnegie Foundation classification. Data was analysed to determine if a correlation exists between the library and student persistence. Library statistics were drawn from the Association of College and Research Libraries (ACRL) Metrics database which provides reports collected from academic institutions. When aggregated, the ACRL report yielded total library expenditures, total salaries of professional staff, the professional staff full-time equivalent (FTE), fall semester student enrolment and data from a library instruction category of ACRL surveys for associate degree-granting institutions. Results - After replicating the same mathematical approach, the single category that has remained constant for all institutions is professional staff. While the former study’s analysis suggested that a relationship between library expenditure and retention existed in every Carnegie category, this study asserts that the same argument cannot be made for master’s degree-granting institutions. The findings here indicate that total library and professional salary expenditure had a negative correlation. Also, while an analysis of instruction at the two-year school level cannot make the case that expenditure and staffing significantly influence retention, they can justify that instruction plays a factor in whether a student persists with their education. Conclusion - The current research posits that there is no longer a relationship between library expenditure per se and student retention. Further research is needed to resolve the differences in the results of the study. Since there is a correlation between library instruction and retention at the two-year college, high-impact information literacy activities can form a bond between the student and the institution. Considering the low retention rates at the two-year school, a customised library instruction approach may be a solution to improving retention.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".