Positive Correlation Between Academic Library Services and High-Impact Practices for Student Retention
Bibliographic record
Abstract
A Review of: Murray, A. (2015). Academic libraries and high-impact practices for student retention: Library deans’ perspectives. portal: Libraries and the Academy, 15(3), 471-487. http://dx.doi.org/10.1353/pla.2015.0027 Abstract Objective – To investigate the perceived alignment between academic library services and high-impact practices (HIPs) that affect student retention. Design – Survey questionnaire. Setting – Public comprehensive universities in the United States of America with a Carnegie classification of master’s level as of January 2013. Subjects – 68 library deans or directors out of the 271 who were originally contacted. Methods – The author used Qualtrics software to create a survey based on the HIPs, tested the survey for reliability, and then distributed it to 271 universities. Library services were grouped into 1 of 3 library scales: library collection, library instruction, or library facilities. The survey consisted of a matrix of 10 Likert-style questions addressing the perceived level of alignment between the library scales and the HIPs. Each question provided an opportunity for the respondent to enter a “brief description of support practices” (p 477). Additional demographic questions addressed the years of experience of the respondent, undergraduate student enrollment of the university, and whether librarians held faculty rank. Main Results – The author measured Pearson correlation coefficients and found a positive correlation between the library scales and the HIPs. All three library scales displayed a moderately strong positive correlation between first-year seminars and experiences (HIP 1), common intellectual experiences (HIP 2), writing-intensive courses (HIP 4), undergraduate research (HIP 6), diversity and global learning (HIP 7), service learning and community-based learning (HIP 8), internships (HIP 9), and capstone courses and projects (HIP 10). The library collections scale and library facilities scale displayed a moderately strong correlation with learning communities (HIP 3) and collaborative assignments and projects (HIP 5). The library instruction scale displayed a strong positive correlation with HIP 3 and a very strong positive correlation with HIP 5. Each of the positive correlations was of high significance. As the rating of library alignment with each HIP increased, so did the total rating of each library scale. Along with the quantitative data, various themes for each HIP relating to the library’s support practices emerged from the qualitative feedback. No significant trends were noted from the demographic questions. Conclusion – Library deans or directors can utilize the conceptual framework presented in this study to connect the impact of library services to terminology and practices commonly understood by university administrators. Further research using the conceptual framework would benefit future discussion on how academic libraries measure impact or success of their library services.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".