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Record W1532774936 · doi:10.18438/b8h60g

Students and Libraries May Benefit from Late Night Hours

2015· article· en· W1532774936 on OpenAlexaffvenue
Richard Hayman

Bibliographic record

VenueEvidence Based Library and Information Practice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsMount Royal University
Fundersnot available
KeywordsLibrary scienceMedical educationPsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

A Review of:
 Scarletto, E. A., Burhanna, K. J., & Richardson, E. (2013). Wide awake at 4 AM: A study of late night user behavior, perceptions and performance at an academic library. Journal of Academic Librarianship, 39(5), 371-377. http://dx.doi.org/10.1016/j.acalib.2013.02.006
 
 Abstract 
 
 Objective – To assess late night library usage, including a demographic profile of students benefitting from late night hours, with an analysis of the services and resources they used, and whether the use of late hours is connected to student success.
 
 Design – A mixed-methods approach including quantitative demographic information alongside qualitative user feedback collected using a web-based survey.
 
 Setting – A large, public research university library in the United States of America using late night operating hours (11 P.M. to 7:30 A.M.) to create 24-hour library availability 5 days per week.
 
 Subjects – Undergraduate and graduate students.
 
 Methods – Using the university’s building monitoring database (BMD), researchers collected data on which students were using the library building when late night hours were in effect for fall and spring semesters. Along with the date and time of entry, the BMD collected the university ID number of the students and their email address. Using student ID numbers, information from the BMD was cross-referenced with anonymized demographic information from the university’s institutional planning office, enabling comparisons across a range of other data, including students’ discipline, GPA, and other information.
 
 Researchers emailed students the web-based survey, directly targeting users who had made use of the library’s late night operating hours. Survey questions investigated when students used the library, explored student aims when in the library, and asked students to rank the tasks they were trying to accomplish while in the library. In addition, researchers sought student feedback on what services and resources they used during late hours, asking students to identify services and resources they would have liked to use but which were not offered during late night hours, and inquiring about students’ sense of safety and security when using the library late at night. 
 
 Main Results – In total, researchers report that 5,822 students, representing approximately 21% of the campus population, visited the library during the late hours, for 22,383 visits. Researchers report that 57% of late night users took advantage of the extended hours on more than one occasion, with 39% returning three or more times. Sundays were the most popular day, while Thursdays were least popular. Researchers also tracked entry times, with the most popular entry times occurring between 11 P.M. to 2 A.M., accounting for 80% of all late night visits. While survey respondents were drawn entirely from the late night users, 63% preferred using the library late at night versus standard daytime operating hours.
 
 The overall survey response rate was less than 5%. Survey respondents (n=243) reported participating in a variety of activities while visiting during late night library hours, with quiet study (87%), working on projects or papers (72%), and group study (42%) as the most frequently reported activities. Respondents also ranked the top three activities they hoped to accomplish while in the library: quiet study (50%) and work on projects and papers (34%) remained top activities, though group study (20%) fell to fourth place, slightly behind the third-ranked activity of printing or copying documents (22%). 
 
 Respondents reported their use of services during late night hours, indicating use of the university’s wireless Internet access, library printers, computers, and online databases and electronic resources. The only staff service point available to students during overnight hours, circulation services, was used by 16% of respondents. Regarding student responses about what services were not offered that they would have liked to have available, the researchers reveal that “[f]ood and drink were overwhelmingly the most frequently requested services” (p. 374), followed by a desire for more comfortable furniture and spaces. Some respondents also requested that late night hours be extended to seven days a week. Overall, 96% of those submitting user feedback reported a sense of security, an important consideration for late night hours. 
 
 Regarding the sample, researchers found that the population of late night library users closely reflected the overall university population. An independent samples t-test comparing the differences between the average GPA of late night users to the average GPA of the overall university population was statistically insignificant. For undergraduates only, there exists a small difference between the retention rate of students using late night hours (84.6%) versus overall university retention rate (80.2%). A Pearson’s chi-squared test revealed a statistically significant association between late night library use and retention rates for both undergraduate and graduate populations when compared to the retention rates of non-late night users across the same student groups.
 
 Conclusion – This study reveals that undergraduate students in particular make use of late night hours for studying or other academic activities, and positions the late night model as a successful customer service offering at Kent State University. Although researchers do conclusively connect the availability of late night hours to student retention and academic success, their study points to the need for further research exploring this question.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0030.787
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.307
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations2
Published2015
Admission routes2
Has abstractyes

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