Varying Student Behaviours Observed in the Library Prompt the Need for Further Research
A Review of:
Paretta, L. T., & Catalano, A. (2013). What students really do in the library: An observational study. The Reference Librarian, 54(2), 157-167. doi:10.1080/02763877.2013.755033
Medical Librarian, Independent Contractor
Port Clinton, Ohio, United States of America
Received: 25 Oct. 2013 Accepted: 10 Jan. 2014
2014 Melssen. This is an Open Access article distributed under the terms of the Creative Commons‐Attribution‐Noncommercial‐Share Alike License 2.5 Canada (http://creativecommons.org/licenses/by‐nc‐sa/2.5/ca/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one.
Objective – To determine if the behaviours of students studying in the library are primarily study or non-study related, the extent to which these behaviours occur simultaneously, what types of study and non-study behaviours are most common, and if the time of day or use of social media have an effect on those behaviours.
Design – Observational study.
Setting – Two university libraries in New York.
Subjects – A total of 730 university students.
Methods – Two librarians at 2 separate university libraries observed and recorded the behaviours of 730 students. Observations were conducted over the course of several weeks during the Fall of 2011 in the designated study or quiet areas, reference room, and at computer terminals of the libraries. Observations were made by walking past the students or by observing them from a corner of the room for between 3 to 10 seconds per student. Student activities were recorded using a coding chart. The librarians also collected data on the perceived age, gender, and ethnicity of the students and whether the students were using a computer at the time of observation. If students displayed more than one behaviour during a single observation, such as talking on the phone while searching the library’s online catalogue, the first behaviour observed or the behaviour that was perceived by the observer to be the dominant behaviour was coded behaviour 1.The second behaviour was coded behaviour 2.
Main Results – The behaviours of 730 students were observed and recorded. Two librarians at separate universities were responsible for data collection. Kappa statistical analysis was performed and inter-rater reliability was determined to be in agreement. Data was analyzed quantitatively using SPSS software.
Over 90% of students observed were perceived to be under 25 years of age and 56% were women. The majority were perceived to be white (62%).
Of the 730 observations, 59% (430) were study related and 37% (300) were non-study related. The most common study related behaviours included reading school-related print materials (18.8%) and typing/working on a document (12.3%). The most common non-study related behaviours included Facebook/social media (11.4%) and website/games (9.3%). The least common study related behaviour was using the school website (1.2%) and the least common non-study related behaviour was “other on the phone” (0.1%).
Second behaviours were observed in 95 of the 730 students observed. Listening to music was the most common second behaviour (35.8%) and educational website was the least common (1.1%).
Most study observations were made on Mondays and most non-study observations were made on Thursdays and Fridays. Throughout the entire day, study related behaviours were observed between 62-67% of the time regardless of the time of day. Students working on computers were more likely to be observed in engaging in non-study related behaviour (73%) than those not working on a computer (44%).
Conclusion – Students display a variety of study and non-study behaviours throughout the day with the majority of the behaviours being study related. Students also blend study and non-study activities together, as evident in their switching between study and non-study related behaviours in a single observation and their ability to multitask. Data gathered from this study provides evidence that students view the library as not only a place for study but also a place for socialization.
Several limitations of this study are acknowledged by the authors. First, behaviours that appear to be non-study related, such as watching videos on YouTube, could be study related. Many faculty members utilize social media tools such as Facebook, Twitter, and YouTube to support their course content. A student observed watching YouTube videos could be watching a professor’s lecture, not a video for entertainment purposes only. This lack of knowing definitively why students are utilizing social media while in the library may have led the authors to mistake non-study behaviour for study behaviour.
An additional limitation is the short duration of time spent observing the students as well as the proximity of the observer to the student. Observations lasting longer than 3 to 10 seconds and made at a closer range to the students could provide more accurate data regarding what type of behaviours students engage in and for how much time. In addition to the before mentioned limitations, the authors acknowledge that they had no way of knowing if the individuals being observed were actual students: the assumed students could have been faculty, staff, or visitors to the university.
Due to the study’s limitations, further research is needed to determine in greater detail what students are doing while they are studying in the library. This data would allow librarians to justify the need to provide both study and non-study space to meet the diverse needs of students. Conducting a cohort study would allow researchers to observe student behaviour longitudinally. It would minimize the limitations of short-term student observation as well as the proximity of the observer to the student. Research on the use of mobile technologies by students, such as smart phones, to access study related material while they are in the library would also yield valuable data regarding student study behaviours.
Research indicates that students do not accurately report their study behaviours. A student may claim to study for three consecutive hours in the library; however, two of the three hours could be spent texting friends or checking Twitter. The authors sought to determine if their own students spend more of their time studying or engaging in non-study related behaviours.
Critical appraisal of this study was completed using the Evidence Based Library and Information Practice Critical Appraisal Checklist (Glynn, 2006). The study’s validity was analyzed in four content areas: population, data collection, study design, and results. While the results are valid, the validity of the population selection, study design, and data collection methods are questionable.
The selection of study participants is problematic due to lack of information on the student population size. The authors are also not clear if an equal number of observations were made at both libraries. Knowing the student population size and if a comparable number of observations were made at both institutions would help determine if 730 participants is a large enough sample size and if all possible study participants are represented.
An additional concern is the study design. The authors acknowledge that it is possible some of the students observed could have been faculty, staff, or visitors. This confounding variable could have been addressed if inclusion and exclusion criteria of study participants were clearer or if a different study design that allowed for identifying students from non-student library users was chosen. Also of concern is the author’s lack of explanation as to why data on ethnicity, age, or gender was significant to the study.
One of the main objectives of the study was to determine if such factors as time of day affect study behaviours. Detail regarding the types of behaviours is provided, however, minimal information on time of day is given. Having access to a copy of the coding instrument used in addition to a clearer description of the data collection methods would strengthen the face validity of the study and allow other researchers to replicate the study.
Despite these issues, results were clearly explained. Ethics approval was obtained and informed consent was not necessary for this study. Kappa analysis minimized inter-rater bias and the study was validated through pilot testing. Opportunities for further research were identified by the authors.
Accurate data regarding student behaviours in the library allows librarians to better understand the diverse needs of students and provide library resources as well as services to meet those needs. The challenge is how librarians can determine what exactly students are doing in the library. This study inspires further research on the use of observational studies.
Glynn, L. (2006) A critical appraisal tool for library and information research. Library Hi Tech, 24 (3), 387-399. doi:10.1108/07378830610692154
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