Valued Academic Library Services Are Not Necessarily the Ones That Are Used Most Frequently, Students’ Service and Social Media Communication Priorities Should Also Be Considered
Bibliographic record
Abstract
A Review of: Stvilia, B., & Gibradze, L. (2017). Examining undergraduate students' priorities for academic library services and social media communication. The Journal of Academic Librarianship, 43(3), 257-262. https://doi.org/10.1016/j.acalib.2017.02.013 Abstract Objective – To examine how undergraduate students rate the importance of different categories of library services and library social media postings. Design – Online survey. Setting – Large research university in the United States. Subjects – 159 undergraduate students enrolled in 3 information technology classes. Methods – Participants were asked to rate the importance of different library service categories on a 7-point Likert scale. The library service categories were (1) access to information and computer resources, (2) study support services, (3) support for club meetings, and (4) Q&A services. Participants were also asked to rate the importance of nine different categories of library social media postings, also on a 7-point Likert scale. The categories of social media postings were (1) event, (2) resources, (3) community building, (4) operations updates, (5) study support, (6) Q&A, (7) survey, (8) staff, and (9) club. Students were also asked to identify which library services they currently use. Main Results – Validly submitted surveys totaled 104 (response rate 65%). Respondents rated access to information and computer resources (M=5.9) and study support services (M=5.9) as being of the highest importance, with no statistically significant difference being found between these ratings. Respondents rated Q&A services (mean not reported) and support for club meetings (M=4.8) as being of significantly lower importance than the baseline (access information and computer resources). In terms of service usage, using the library to study (87%) and to access information and computer resources (59%), were the top two most reportedly used services. Respondents rated social media postings relating to operations updates (M=5.6), study support (M=5.5) and events (M=5.4) as being of highest importance, with no significant difference between the ratings of these three categories. Respondents rated all other categories of social media postings (survey, M=4.7; staff, M=4.4; means for remaining categories not reported) as being of significantly less importance than the baseline (operations updates). For just over half the social media posting categories (5/9, 56%) importance rankings found in this study agree with engagement rankings the authors found in a previous study (Stvilia & Gibradze, 2014). Conclusion – The results of this study suggested frequency of use alone cannot be used to determine the value students place on a library’s services, as students may perceive equal value in services they use at different frequencies. The authors, therefore, argued there is a strong need to inexpensively predict users’ perceptions of service value without relying on usage metrics alone. Because a level of agreement was found between social media engagement (determined in the authors’ 2014 study) and importance rankings (found in this study), the authors proposed further research be done to determine whether and how an analysis of library social media engagement can be used as an inexpensive way to predict the perceived importance and value of a library’s services. While the authors recognized it may not be appropriate to generalize the results of this study to a wider student population, they suggested the findings may be applicable to similar groups of students (i.e., undergraduate information technology students).
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".