Longitudinal Associations between Online Usage of Library-Licensed Content and Undergraduate Student Performance

Felichism Kabo, Annaliese Paulson, Doreen Bradley, Ken Varnum, Stephanie Teasley*

Seeking to better understand the longitudinal association between online usage of library-licensed content and short- and long-term student performance, we linked EZproxy logs to institutional university data to study how library usage impacts semester and cumulative GPAs. Panel linear mixed effects regression models indicate online library usage is significantly associated with both semester and cumulative GPAs. The library usage effect is larger for semester GPA, and varies by on- and off-campus residency. The effect on semester GPA is larger for off-campus students, while for cumulative GPA the effect is larger for on-campus students. Longitudinally linked library-institutional data offers key insights on the library’s value.

Introduction

Library usage is correlated with important undergraduate student outcomes including academic performance and retention. However, the relationship between library usage and academic performance is better understood over the short term, and for specific subsets of students, such as first-year undergraduate students.1 We need to develop a better understanding of this relationship both over the long term, and for all undergraduate students. One reason for our currently limited understanding of this relationship is that, in most universities—owing to privacy concerns—libraries either do not collect or retain user data with identifiers. This makes it impossible to link library usage data with other institutional or administrative data from the university, including data regarding academic success and retention. Another limitation is that library usage data are often collected as very large logs (millions and billions of records) that may require the application of methodological approaches, such as Big Data techniques, to structure and store in ways that make them more amenable to analysis. Therefore, there is a need for empirical, longitudinal studies that not only use identifiable library data, but also employ Big Data and statistical methods to advance our understanding of the library’s contribution to student success. In this paper, we present the results of a longitudinal study of the association between online library resource usage and student performance for the entire population of undergraduates enrolled at the University of Michigan (U-M) between 2016 and 2019.

The privacy concerns described above are valid; however, other research domains—for which the potential risk of unintended exposure is higher than those of library usage data, such as the type of patient health information covered by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) —have found ways to successfully handle data while maintaining privacy. Yet, these advances in the biomedical and social sciences, which would better serve the privacy requirements of library professional ethics, are still not widely known in libraries. Fortunately, many libraries now adopt the best privacy practices from the social and biomedical sciences. These initiatives make it possible to employ Big Data methods in longitudinal studies of the links from library usage to academic outcomes for the entire student body.

There are two such initiatives critical to the work described in this paper: first, after a multi-year process of engaging with a diverse set of stakeholders including the U-M Learning Analytics Task Force, the U-M Library revised its privacy policy in 2016 to allow the collection and retention of identifiable library usage data;2 second, the Library Learning Analytics Project (LLAP)—funded by the Institute of Museum and Library Services (IMLS)—examined how libraries impact learning outcomes including in course instruction. Learning processes require that members of the university community engage in activities such as accessing digital data and publication repositories, conducting literature reviews, managing citations, and creating data management plans. These activities often entail interacting with the library virtually, such as when accessing and retrieving library licensed content through the proxy server. This paper reports on analyses performed on the links between off-campus, or off-network, electronic usage of library resources, as well as undergraduate academic performance over the short- and long-term. The best context for work of this nature is one in which library users have agency with how they engage with the library services in question. For library licensed content, individuals can access these resources via computers that are on-campus (physically located in the library or elsewhere in the university), or virtually via the proxy server should they choose to use these resources when off-campus. For this reason, the authors limited the analysis to the relationship between online library usage and student outcomes to the time before the COVID-19 pandemic. That is, the study focuses on when students had the choice of accessing library licensed content through on- or off-campus means.

Literature Review

This work is informed by models of information behavior,3 which describes how individuals seek and utilize information.4 Information behavior is contingent on factors such as social contexts, socio-demographics, individual expertise, as well as access to, and ease of use of, technology.5 The work also builds on two lines of inquiry: 1) research into the associations between college residence and academic performance; 2) work on digital inequalities or the digital divide. We examine the link from library usage to student outcomes in two ways: first, defining library usage in terms of use of licensed online content provided by the library, and second, evaluating the impacts of on-campus residency for access to library and other resources and reliable internet.

Research on campus residency has examined the issue of whether there are gains in learning and academic performance from living on- versus off-campus. A study of nearly 95,000 first year students in the United States found living on-campus was significantly associated with a range of learning variables, even though the residency effect size was small to medium.6 An earlier study of first-year students found that the benefits of on-campus residency on academic performance were different across, and within, racial groups. For example, Black students who lived on-campus had significantly higher grade point averages (GPAs) than Black students who lived off-campus.7 Approaching the issue from a different angle, a study of the causal link between campus residency and academic outcomes found living in university-owned housing had a positive association with student retention.8 This finding was in line with prior analysis that established an association between on-campus living and academic performance and student retention for first-year students.9 However, an important caveat is that students who were better prepared academically were more likely to live on-campus as opposed to off-campus.10 Most studies of the link between on-campus residence and student persistence are based on four-year institutions. One exception is a quasi-experimental analysis of community college students that found that living on-campus was associated with a significant increase in upward transfer (to a four-year institution) and, subsequently, bachelor’s degree completion rates.11 However, the association between on-campus residence and academic outcomes is not always positive. A study conducted at a public four-year university in the southeast United States found that commuter or off-campus students had higher GPAs than residential or on-campus students.12

Demographic, geographic, and economic factors all help shape digital disparities in American K-16 education. These disparities are commonly referred to as the “digital divide,” or the gap between those who have access to the internet and other information and communication technologies (ICT), and those who do not. Digital inequalities and disparities affect a broad range of life opportunities and outcomes beyond education, such as economic activity and health care.13 In education, digital inequalities and disparities are a life-course issue and affect disadvantaged students. Their effects are felt from early14 to late in the K-16 pipeline.15 The increasing use of technology inside and outside the classroom has significant ramifications for the digital divide and its effect on student performance. Importantly, some groups of students are systematically more likely to experience digital disparities than others. For example, in 2015, higher percentages of students who were White (66%) used the internet at home compared to Black (53%), Hispanic (52%), and American Indian/Alaska Native (49%) students.16 American Indian/Alaska Native students are more likely than other racial groups to have no internet access, or to have only dial-up internet access at home.17 The interaction of demography and geography disadvantages some students further. While 18 percent of all students in remote rural areas did not have internet access, or had only dial-up access in 2015, a much larger percentage of Black (41%) students in remote rural areas did not have internet access compared to White (13%) and Asian (11%) students. Having no or low-bandwidth internet is detrimental to any form of online learning. For example, students cannot participate in classes offered via video meeting systems that rely on high-speed internet.18 The COVID-19 pandemic worsened the effects of the digital divide, such as for rural students.19 Students of color have been especially impacted by the pandemic and, as noted earlier, are more likely to lack access to reliable broadband internet, and even computers. The pandemic exacerbated existing educational disparities for minority students and likely widened the achievement gap for students of low socioeconomic status.20

In the United States, the effects of the pandemic on the digital divide have demonstrably impacted the entire K-16 pipeline. There were varied institutional responses across the American higher education landscape. Perversely, these varied responses present opportunities for “quasi-experimental” observations regarding the impact of the digital divide on amplifying disparities in student performance. For example, where many colleges and universities stipulated that students residing on-campus leave these residences, some made allowances for students who could not return home, which thus allowed them to still have access to reliable broadband internet via the institution.21 What was fairly universal, however, was the extent and speed with which university libraries adapted to offering primarily online resources,22 which can only meaningfully be accessed via reliable internet connections. Thus, not only were students no longer able to access the library’s physical collections, but they also no longer had access to the library as a study space, including for group or collaborative activities.23 By examining how “regular” (pre-pandemic) electronic library usage is associated with academic performance, this study may therefore help us better understand the likely impacts of the worsening of the digital divide during the pandemic. Based on evidence that the digital divide has worsened during the pandemic,24 we can reasonably assume that the importance of the relationship between online library usage and academic performance has only increased.

The literature also indicates that models of student performance need to account for other demographic, socioeconomic, and academic factors, including include gender, first-generation status, family or household income, high school GPA, and academic class level. Across national contexts in developed countries, female students are more likely to have both higher work ethics and GPAs than males.25 First-generation students are more likely to contend with barriers to academic success—such as job and family responsibilities and/or inadequate study skills26—and thus tend to have poorer academic outcomes.27 Students who enter college with higher family or household incomes have significantly higher GPAs than those from lower socioeconomic backgrounds.28 High school GPA is a strong predictor of college or university GPA as well, especially in the first year.29 Academic class level is correlated with GPA, as upper class students (e.g. seniors) are more likely to have higher grades, especially in classes that also have lower class students, such as sophomores.30

Theoretical Framework

Building on models of information-seeking behavior, we developed a theoretical framework (Figure 1) that correlates student performance with library usage as captured by EZproxy sessions, controlling for factors like socio-demographics and academic background.31 A key strength of the framework is that it presents testable relationships among demographic and contextual factors, information-seeking behaviors, and academic outcomes.

This paper examines the association between information-seeking behavior (off-campus or off-network electronic library resource use), and both semester and cumulative GPA. However, this relationship must also be understood in the context of contextual factors (“intervening” variables), which contribute to disparities in access to the digital resources that are needed to make effective use of electronic library licensed content. Research shows that access to, and proper use of, digital technology generally has a positive correlation with academic performance; this finding is robust across regional and national settings.33 Based on these findings, we hypothesize that students identified as accessing online library licensed content will have better academic outcomes than those students with no evidence of digital access to these resources. However, there is also evidence that our hypothesized relationship has both short- and long-term implications. While not specific to electronic resources, studies suggest that library usage is positively correlated with student performance both in the short-term,34 and in the long-term.35 Therefore:

H1: Students who electronically access library licensed content will have higher semester GPAs.

H2: Students who electronically access library licensed content will have higher cumulative GPAs.

Methodology

The study sample is all undergraduate students (N = 45,254) who were enrolled at the University of Michigan (U-M) from fall 2016 through winter 2019 (or September 2016 through April 2019). We focus on these six semesters before the pandemic because students had more agency with respect to their usage of electronic library licensed content. That is, students could choose to access materials using computers that are physically on-campus, or off-campus access via the proxy server. We sourced library usage data from EZproxy logs (690,300,076 records) stored in a secure repository that the U-M Library managed. We obtained student demographic and outcome data (GPAs) from the research-focused Learning Analytics Data Architecture (LARC) data set maintained by the U-M Office of Enrollment Management. The project team implemented several measures to protect the privacy and confidentiality of the individuals in the library and LARC data. For example, the library data were classified at the “Restricted” level of data security. This is the highest classification or sensitivity level for U-M institutional data, has the most stringent legal or regulatory requirements, and has the most prescriptive security controls. These controls included restricting access to only two members of the project team, storing and curating the data on a secure enclave, setting up access to the enclave via a terminal in a locked and restricted data room, and requiring that all analyses be performed on the enclave.

Our primary interest in this paper is the relationship between information-seeking behavior (EZproxy sessions) and student performance. EZproxy is proxy server software that many academic libraries use to give authenticated off-campus users access to electronic resources licensed by the library as if they were on campus. After authenticating to a campus system, off-campus users receive an on-campus IP address and are then considered to be a member of the campus community by the information provider. The authors cleaned and normalized raw, unstructured EZproxy logs using Python scripts and regular expressions, and then entered the data into a relational database using structured query language (SQL) scripts. Over 80 percent of the EZproxy data have strong university identifiers which enables merges with other administrative data, such as LARC. It is critical to note that EZproxy logs available to the study: a) did not include any on-campus usage, and b) did not include anyone who used the university’s virtual private networks (VPN). Using SQL and R scripts, we merged the data and exported the resultant data set into Stata 16 statistical software for modeling and analysis.36

The theoretical framework shown in Figure 1 suggests that student outcomes are a function of factors, such as race and gender, that apply to all the students in the study (“fixed effects”), and factors, such as academic units or schools, that cluster student behaviors and outcomes (“random effects”). We also accounted for student random effects for unobserved, time invariant factors, such as motivation or grit. Thus, we ran panel linear mixed effects (LME) regression models of the association between library usage and student GPA, contingent on students being enrolled in at least four semesters over the study period.

Variables

The two continuous dependent variables are semester GPA (“SEM_GPA”) and cumulative GPA (“CUM_GPA”). While SEM_GPA is on a 0 – 4.4 scale and CUM_GPA is on a 0 – 4.314 scale, fewer than 0.5 percent of students have a semester or cumulative GPA that is higher than 4.0. The dichotomous independent variable “EZproxy Session in Term” is coded one if a student is associated with one or more EZproxy sessions during an academic term, and is coded zero otherwise.

We also account, or control, for potential “intervening” variables as follows: the dichotomous variable “On-campus Residence” is coded one if a student was residing in a university residence, and zero otherwise; the variable “High School GPA” is on a continuous 0 – 4 scale and captures a student’s academic performance before enrollment at the university; gender is captured by the dichotomous variable “GENDER” (1 = Female, 2 = Male). Note that the LARC data set used for the study does not account for non-binary options. The effects of race, first generation status, family income, and class level were controlled for using the categorical variables “RACE” (1 = White, 2 = Asian, 3 = Black, 4 = Hispanic, 5 = Two or More, 6 = Other, 7 = Not Indicated), “FIRST GENERATION” (1 = First Gen, 2 = Not First Gen, 3 = Don’t Know), “FAMILY INCOME” (1 = More than $100,000; 2 = Less than $25,000; 3 = $25,000 - $49,999; 4 = $50,000 - $74,999; 5 = $75,000 - $99,999; 6 = Don’t Know; 7 = Missing), and “CLASS LEVEL” (1 = Freshman, 2 = Sophomore, 3 = Junior, 4 = Senior), respectively.

Statistical Modeling

We ran panel LME regression models with random effects for individuals, as well as by school or academic unit (see Table A.7 in the appendix for a list of the 15 schools that undergraduate students were affiliated with). LME models, an extension of simple linear models, are useful when there is non-independence in the data. This arises from, for example, a hierarchical structure in the data, such as when students are sampled from within academic units. Panel regression approaches are necessary when working with longitudinal study designs, where multiple observations are made on each individual subject. LME models have both fixed effects, which are directly estimated and are analogous to standard regression coefficients, and random effects, which in our case take the form of random intercepts. The fixed effects in our LME models correspond to the “intervening” variables. The random effects account for the fact that student behaviors and outcomes may, instead of being uniform across all undergraduates, be grouped by academic units which map onto disciplinary boundaries that likely affect library usage. The random effects also enable us to account for unobserved, time-invariant individual-level factors, such as motivation or grit. Table A.7 in the appendix shows that there are notable differences across schools with respect to the percentage of students who have at least one EZproxy session during an academic term. After each LME model, we ran a likelihood-ratio comparing this model with a one-level ordinary linear regression. This test was highly significant for each of the LME models in our study, supporting the decision to use the LME model.

Findings and Discussion

Descriptive Statistics

Over half of enrolled undergraduates had at least one EZproxy session during an academic term over the study period (Table 1).

There are some notable differences in library usage among enrolled undergraduates. Table 2 below illustrates differences in library usage by demographic, academic, and residency factors for the winter 2019 term (see the appendix for similar statistics on all semesters). Off-campus students are more likely to have at least one EZproxy session in the academic term than are on-campus students. This makes sense because students who are on-campus are more likely to access electronic library resources on the university’s network, in which case authentication is not required. Recall that students are identifiable in the EZproxy logs only when authentication is required. An example of this is when a student accesses electronic library resources outside the university’s network such as from an off-campus residence, coffee shop, etc. There is a significant gender difference, with females much more likely than males to have an EZproxy session, despite more males (69%) than females (66%) residing off-campus in winter 2019. Note that the likelihood of having at least one EZproxy session increases with each class level. Perhaps this is because students are more likely to move or reside off-campus as they progress from freshman to seniors. However, a factor that weakens this explanation is U-M does not require freshmen and sophomores to live on-campus, as is the case in some colleges and universities. An alternative explanation is that lower-level classes are less research-intensive and students may not need library-provided resources to complete research and writing projects.

Finally, there are noteworthy differences between academic units. Additional work would be needed to clarify the factors that account for these differences. For example, 45 percent of engineering undergraduates had at least one EZproxy session compared to 73 percent of art and design undergraduates, even though both academic units are co-located at the university. A potential explanation could be that these differences reflect disciplinary differences (STEM versus arts and humanities). Another plausible explanation could be that the differences reflect gaps in technological expertise between the two groups of students, with engineering students being more likely to access electronic library resources using the university’s VPN which bypasses the authentication process on the library’s proxy server. We should also keep in mind factors such as the interplay between residency and socioeconomic statuses. It is more expensive to live on- rather than off-campus, implying that students in the former group may tend to be from wealthier families. For example, 78 percent of nursing undergraduates had at least one EZproxy session, compared to 41 percent of business administration undergraduates. Tabulations of residency for the two academic units showed that 32 percent of business undergraduates resided on-campus in winter 2019, compared to 20 percent of nursing undergraduates. Similarly, tabulations of family income for these two academic units showed that 58 percent of business undergraduates had a family income of more than $100,000, compared to 48 percent of nursing undergraduates. These findings suggest that library usage data have the potential to reveal disparities and inequalities, and could therefore help libraries make significant analytical contributions of interest to their institutions.

Regression Models

The results from the regression modeling are summarized in Tables 3 (semester GPA) and 4 (cumulative GPA). The regression models showed positive and statistically significant associations between having at least one EZproxy session in an academic term, and both semester and cumulative GPAs, controlling or accounting for residency, race, gender, high school GPA, family income, first generation status, and class level.

Overall, the results from the regression models for semester GPA provide strong support for hypothesis H1. That is, students that use electronic library licensed content have higher semester GPAs. Having an EZproxy session during an academic term was correlated with a 0.14 point increase in semester GPA (model 1). To further examine the impact of campus residency, considering the link between authentication requirements and a student’s presence in the EZproxy logs, we ran separate models for on-campus (model 2) and off-campus (model 3) students. For off-campus students, having an EZproxy session in an academic term is correlated with a 0.17 point increase in semester GPA. In comparison, for on-campus students, having an EZproxy session in an academic term is correlated with a 0.09 point increase in semester GPA. For the other “intervening” variables, it is noteworthy that the GPA gender gap in favor of females is smaller for on-campus students compared to their off-campus peers. Interestingly, notwithstanding the small sizes of the effects, the first-generation disadvantage of lower GPAs is more pronounced for on-campus students relative to their off-campus peers.

Overall, the results from the regression models for cumulative GPA provide strong support for hypothesis H2. That is, students that use electronic library licensed content have higher cumulative GPAs. However, the effect of having at least one EZproxy session in an academic term is smaller for cumulative GPA than it is for semester GPA. Model 4 shows that having an EZproxy session in an academic term was correlated with a 0.02 point increase in cumulative GPA. To examine the effect of being on- or off-campus, we ran separate models for on- (model 5) and off-campus (model 6) students, which show differences between the two groups of students—although in ways that are opposite to semester GPA. Having an EZproxy session in an academic term has a larger effect on cumulative GPA for on-campus students compared to their off-campus peers. However, the magnitude of both effects is very small. Also note that, like semester GPA, the female advantage in cumulative GPA was smaller for on-campus students relative to off-campus students. The first-generation disadvantage with respect to lower cumulative GPAs is more pronounced for on-campus students compared to those that are off-campus.

The study findings suggest that using library resources positively effects academic performance. These effects were larger in magnitude for semester GPA relative to cumulative GPA. For example, regarding semester GPA, first-generation students had a lower GPA (-0.119) than non-first-generation students. Further, males had a lower semester GPA (-0.091) than females. Thus, the impacts of gender and first-generation status on semester GPA were smaller in magnitude than the impact of having at least one EZproxy session during an academic term.

Conclusion

Because library data are often not integrated into other university data, there are major obstacles in demonstrating the richness and complexity of the value of academic library usage for the students who use these resources. We show that merging library usage and student outcome data yields valuable insights on the value of the academic library. Understanding patterns of off-campus use of library resources offers an additional point of insight into potential gaps in use by certain groups of students, such as those living off campus, which may correlate with lower academic success and retention. If students in particular programs tend to live off campus, yet their programs are library-research intensive, what could this mean for those students? For example, 80 percent of undergraduate nursing students live off campus, yet the nursing program integrates the library heavily in its curriculum. We could explore off-campus use by students in this program to potentially identify students at risk of lower academic performance, or to provide indicators to faculty advisors if a student’s GPA in research-intensive courses falls below a certain threshold. As additional data from other library services is collected in the future, libraries can develop models to explore other questions around library usage, student success, and curricular integration. Libraries could use the work by the LLAP and allied initiatives to identify opportunities for mitigating educational disparities. Library usage data adds depth of perspective of the student experience, and student engagement broadly, during undergraduate study, and can therefore be a valuable addition to institutions of higher education as they continue to make data-informed decisions to improve undergraduate education. Further, in the process of doing this work, we have created shareable scripts and tools that could be used to replicate our work in other institutional settings. These and other resources can be downloaded for free from the LLAP project’s GitHub site (https://github.com/Learning-Library-Analytics-Project) and website (https://libraryanalytics.org/).

Libraries are often new participants within campus learning analytics efforts. The research described here could lead to new partnerships between libraries and other institutional organizations. Much as traditional academic advisors and partners have great insight into the specific needs and capabilities of their students, so could libraries better tailor their services to those needs. By being better informed about both the kinds of assignments and the needs of the individual students, along with a more granular conceptualization of the technologies they have access to, library staff could be better situated to deliver information services tailored to individual needs. As noted by researcher Megan Oakleaf, designing library services and instruction for the average student harms almost everyone (Oakleaf et al. 2020).37

Future work could build on our findings by disentangling the effects of students who are off-campus and not using the VPN (and thus need authentication), versus those who are on-campus but choose to access library licensed content via non-university devices, and hence the library proxy server. Undoubtedly there are economic, technical, and experiential factors contributing to these types of differences in accessing library licensed content. Unfortunately, we were not able to capture them in our study. In addition to multiple socioeconomic factors that could impact student use of library licensed content, there are other factors that could account for these differences, such as the varying nature and demands of curricula across programs and colleges. While there is a healthy demand for library curriculum-integrated instruction (CII) at U-M, programs and instructors may require CII at different times in the progression of a student’s academic career. For example, some programs require library CII in first-year experience courses, while other programs may only require CII in the third- or fourth-year. This suggests several lines of future inquiry, such as how course selection affects the need and motivation to use library-licensed resources, or even how the level of study (such as first-year, third-year, and so on) correlates to use of licensed resources and, subsequently, to academic outcomes.

Acknowledgement

The work described in this paper is primarily supported by funding from the Institute of Museum and Library Services (IMLS, LG-96-18-0040-18), and secondarily by the University of Michigan Library.

Appendix

Tables A.1 – A.7 show the percentages of students who had at least one EZproxy session in an academic term by various sociodemographic and academic factors.

Notes

1. Krista M. Soria, Jan Fransen, and Shane Nackerud, “Library Use and Undergraduate Student Outcomes: New Evidence for Students’ Retention and Academic Success,” portal: Libraries and the Academy 13, no. 2 (2013): 147–64, https://doi.org/10.1353/pla.2013.0010.

2. Laurie Alexander, Doreen R. Bradley, and Kenneth J. Varnum, “On the Road to Learning Analytics: The University of Michigan Library’s Experience with Privacy and Library Data,” in Using Digital Analytics for Smart Assessment, ed. Tabatha Farney (Chicago: American Library Association, 2018), 83–93.

3. T. D. Wilson, “Models in Information Behaviour Research,” Journal of Documentation 55, no. 3 (1999): 249–70, https://doi.org/10.1108/EUM0000000007145; J. David Johnson, “Cancer-related information seeking.” Health Communication (Cresskill, N.J.: Hampton Press, 1997); Thomas D. Wilson, “Information Behavior Models,” in Encyclopedia of Library and Information Science, 4th Edition (CRC Press, 2017), 2086-2093.

4. Marcia J. Bates, “Information Behavior,” in Encyclopedia of Library and Information Science, 4th Edition (CRC Press, 2017), 2074-2085.

5. Bates, “Information Behavior,”; Lotta Haglund and Per Olsson, “The Impact on University Libraries of Changes in Information Behavior among Academic Researchers: A Multiple Case Study,” The Journal of Academic Librarianship 34, no. 1 (2008): 52–9, https://doi.org/10.1016/j.acalib.2007.11.010; Xi Niu and Bradley M. Hemminger. “A Study of Factors that Affect the Information-seeking Behavior of Academic Scientists,” Journal of the American Society for Information Science and Technology 63, no. 2 (2012): 336–53, https://doi.org/10.1002/asi.21669.

6. Polly A. Graham, Sarah Socorro Hurtado, and Robert M. Gonyea, “The Benefits of Living on Campus: Do Residence Halls Provide Distinctive Environments of Engagement?” Journal of Student Affairs Research and Practice 55, no. 3 (2018): 255–69, https://doi.org//10.1080/19496591.2018.1474752.

7. Ruth N. López Turley and Geoffrey Wodtke. “College Residence and Academic Performance: Who Benefits from Living on Campus?” Urban Education 45, no. 4 (2010): 506–32, https://doi.org/10.1177/0042085910372351.

8. Lauren T. Schudde, “The Causal Effect of Campus Residency on College Student Retention,” Review of Higher Education 34, no. 4 (2011): 581–610, https://doi.org/10.1353/rhe.2011.0023.

9. Clare Huhn, “The “Housing Effect” on First-Year Outcomes,” (Madison, WI: Academic Planning and Analysis, Office of the Provost, University of Wisconsin-Madison, 2006).

10. Ibid.

11. Jonathan M. Turk and Manuel S. González Canché, “On-Campus Housing’s Impact on Degree Completion and Upward Transfer in the Community College Sector: A Comprehensive Quasi-Experimental Analysis,” The Journal of Higher Education 90, no. 2 (2019): 244–71, https://doi.org/10.1080/00221546.2018.1487755.

12. Denise Balfour Simpson and Dana Burnett, “Commuters Versus Residents: The Effects of Living Arrangement and Student Engagement on Academic Performance,” Journal of College Student Retention: Research, Theory & Practice 21, no. 3 (2017): 286–304, https://doi.org/10.1177/1521025117707516.

13. Laura Robinson et al., “Digital Inequalities and Why They Matter.” Information, Communication & Society 18, no. 5 (2015): 569–82, https://doi.org/10.1080/1369118X.2015.1012532; Xinzhi Zhang et al. “Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century,” Ethnicity & disease 27, no. 2 (2017): 95–106, https://doi.org/10.18865/ed.27.2.95.

14. Paul F. Cleary, Glenn Pierce, and Eileen M. Trauth, “Closing the Digital Divide: Understanding Racial, Ethnic, Social Class, Gender and Geographic Disparities in Internet Use among School Age Children in the United States,” Universal Access in the Information Society 4, no. 4 (2006): 354–73, https://doi.org/10.1007/s10209-005-0001-0; Linda A. Jackson et al., “Does Home Internet Use Influence the Academic Performance of Low-income Children?” Developmental Psychology 42, no. 3 (2006): 429–35, https://doi.org/10.1037/0012-1649.42.3.429.

15. Elizabeth F. Farrell, “Among Freshmen, a Growing Digital Divide,” The Chronicle of Higher Education February 2, 2005, A32-; Steve Jones, Camille Johnson-Yale, Sarah Millermaier, and Francisco Seoane Pérez, “U.S. College Students’ Internet Use: Race, Gender and Digital Divides,” Journal of Computer-Mediated Communication 14, no. 2 (2009): 244–64, https://doi.org/10.1111/j.1083-6101.2009.01439.x.

16. Angelina KewalRamani et al., Student Access to Digital Learning Resources outside of the Classroom. NCES 2017-098. Edited by American Institutes for Research. National Center for Education Statistics. Washington, DC: U.S. Department of Education, 2018.

17. Lauren Musu, “The Digital Divide: Differences in Home Internet Access,” in NCES Blog, edited by NCES Blog Editor. Washington, DC: National Center for Education Statistics, October 18, 2018, https://nces.ed.gov/blogs/nces/post/the-digital-divide-differences-in-home-internet-access.

18. Ana-Paula Correia, “Healing the Digital Divide During the COVID-19 Pandemic,” Quarterly Review of Distance Education 21, no. 1 (2020): 13–21.

19. John Lai and Nicole O. Widmar, “Revisiting the Digital Divide in the COVID-19 Era,” Applied Economic Perspectives and Policy 43, no. 1 (2021): 458–64, https://doi.org/10.1002/aepp.13104.

20. Fawzia Reza, “COVID-19 and Disparities in Education: Collective Responsibility Can Address Inequities,” Knowledge Cultures 8, no. 3 (2020): 68–75, https://doi.org/10.22381/KC83202010; Megan Kuhfeld et al., “Projecting the Potential Impact of COVID-19 School Closures on Academic Achievement,” Educational Researcher 49, no. 8 (2020): 549–65, https://doi.org/10.3102/0013189X20965918.

21. Terence Day et al. “The Immediate Impact of COVID-19 on Postsecondary Teaching and Learning,” The Professional Geographer 73, no. 1 (2021): 1–13, https://doi.org/10.1080/00330124.2020.1823864.

22. Ibid.

23. Ibid.; Dipti Mehta and Xiaocan Wang, “COVID-19 and Digital Library Services – A Case Study of a University Library,” Digital Library Perspectives 36, no. 4 (2020): 351–63, https://doi.org/10.1108/DLP-05-2020-0030.

24. Ana-Paula Correia, “Healing the Digital Divide During the COVID-19 Pandemic,” Quarterly Review of Distance Education 21, no. 1 (2020): 13–21.

25. Kyong Hee Chee, Nathan W. Pino, and William L. Smith, “Gender Differences in the Academic Ethic and Academic Achievement *,” College Student Journal 39, no. 3 (September 2005): 604+; Michael Sheard, “Hardiness Commitment, Gender, and Age Differentiate University Academic Performance,” British Journal of Educational Psychology 79, no. 1 (2009): 189–204. https://doi.org/10.1348/000709908X304406; Arna Kristín Harðardóttir, Sigurður Guðjónsson, Inga Minelgaite, and Kári Kristinsson, “Ethics as Usual? Gender Differences in Work Ethic and Grades,” Management: Journal of Contemporary Management Issues 24, no. 2 (2019): 11–21, https://doi.org/10.30924/mjcmi.24.2.2.

26. Michael J. Stebleton and Krista M. Soria, “Breaking Down Barriers: Academic Obstacles of First Generation Students at Research Universities,” The Learning Assistance Review 17, no. 2 (2012): 7+.

27. Xi Wang, Minhao Dai, and Robin Mathis, “The Influences of Student- and School-level Factors on Engineering Undergraduate Student Success Outcomes: A Multi-level Multi-school Study,” International Journal of STEM Education 9, no. 1 (2022): 23, https://doi.org/10.1186/s40594-022-00338-y.

28. John M. Trussel and Lisa Burke-Smalley, “Demography and Student Success: Early Warning Tools to Drive Intervention,” Journal of Education for Business 93, no. 8 (2018): 363–72, https://doi.org/10.1080/08832323.2018.1496893; Julian R. Betts and Darlene Morell, “The Determinants of Undergraduate Grade Point Average: The Relative Importance of Family Background, High School Resources, and Peer Group Effects,” The Journal of Human Resources 34, no. 2 (1999): 268–93, https://doi.org/10.2307/146346.

29. Siu-Man Raymond Ting and Tracy L. Robinson, “First-year Academic Success: A Prediction Combining Cognitive and Psychosocial Variables for Caucasian and African American Students,” Journal of College Student Development 39, no. 6 (1998): 599–610; Betts and Morell, “The Determiniants of Undergraduate Grade Point Average”; Andrew P. Barkley and Jerry J. Forst, “The Determinants of First-Year Academic Performance in the College of Agriculture at Kansas State University, 1990–1999,” Journal of Agricultural and Applied Economics 36, no. 2 (2004): 437–48, https://doi.org/10.1017/S1074070800026729.

30. Forrest E. Huffman, “Student Performance in an Undergraduate Advanced Real Estate Course: Real Estate Majors vs. Finance Majors,” Journal of Real Estate Practice and Education 14, no. 2 (2011): 111–23, https://doi.org/10.1080/10835547.2011.12091693.

31. Wilson, “Models in Information Behaviour”; Johnson, Cancer-related information seeking.

32. Ibid.; Johnson, J. David. Cancer-related information seeking. Cresskill, N.J.: Health Communication. Cresskill, N.J.: Hampton Press, 1997.

33. Jerry Chih-Yuan Sun and Susan E. Metros, “The Digital Divide and Its Impact on Academic Performance,” US-China Education Review A 1, no. 2 (2011): 153–61; Joanna Goode, “The Digital Identity Divide: How Technology Knowledge Impacts College Students,” New Media & Society 12, no. 3 (2010): 497–513, https://doi.org/10.1177/1461444809343560; Laura Robinson, Øyvind Wiborg, and Jeremy Schulz, “Interlocking Inequalities: Digital Stratification Meets Academic Stratification,” American Behavioral Scientist 62, no. 9 (2018): 1251–72, https://doi.org/10.1177/0002764218773826; KewalRamani et al., Student Access to Digital Learning Resources.

34. Penny Beile, Kanak Choudhury, Rachel Mulvihill, and Morgan Wang, “Aligning Library Assessment with Institutional Priorities: A Study of Student Academic Performance and Use of Five Library Services,” College & Research Libraries 81, no. 3 (2020): 435–58, https://doi.org/10.5860/crl.81.3.435; Soria, Fransen, and Nackerud, “Library Use and Undergraduate Student Outcomes.”

35. (Soria, Fransen, and Nackerud 2014, 2016; Wong and Webb 2011); Krista M. Soria, Jan Fransen, and Shane Nackerud, “Stacks, Serials, Search Engines, and Students’ Success: First-Year Undergraduate Students’ Library Use, Academic Achievement, and Retention,” The Journal of Academic Librarianship 40, no. 1 (2014): 84–91, https://doi.org/10.1016/j.acalib.2013.12.002; Krista M. Soria, Jan Fransen, and Shane Nackerud, “Beyond Books: The Extended Academic Benefits of Library Use for First-Year College Students,” College & Research Libraries 78, no. 1 (2016): 8–22, https://doi.org/10.5860/crl.78.1.8; Shun Han Rebekah Wong and T. D. Webb, “Uncovering Meaningful Correlation between Student Academic Performance and Library Material Usage,” College & Research Libraries 72, no. 4 (2011): 361–70, https://doi.org/10.5860/crl-129.

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37. Megan Oakleaf et al., Connecting Libraries and Learning Analytics for Student Success, (Syracuse, NY: Syracuse University, 2020), https://library.educause.edu/-/media/files/library/2020/12/cllassfinalwhitepaper.pdf.