Saturday, 22 July 2017
Essay: an overview of research into library use and student outcomes
In the challenging and constantly changing environment of higher education, academic libraries are increasingly being tasked with demonstrating the value they provide to the university and its wider stakeholders. In the past, the assessment of library value was measured by inputs and outputs, which are mostly concerned with internal library processes and outcomes; however, to better align with institutional mission and goals, academic libraries are being forced to look outward and must now articulate and provide evidence of their value outside of the library. Therefore, it is no longer reasonable for academic libraries to take their role for granted as “the heart of the university” (Stemmer and Mahan, 359). This shift in the assessment of library value has necessarily also lead to a shift in the research, which has presented opportunity for impact studies to examine the relationship between use of library resources and student outcomes.
Impact studies can be described as ‘analyses that [seek to] demonstrate an alignment of library activity with the mission of the institution’ (Revill in Allison, 2015, p.31). The purpose of this research is to provide an overview of the current state of the literature on one aspect of library activity – that is, library use. In this way, this paper seeks to describe the current state of research into the relationship between use of library resources and student outcomes. This paper also sets out the current research agenda for library assessment and provides an overview of opportunities and future steps for professional development, so that librarians have the core competencies to better document and communicate to stakeholders the impact the library has on student outcomes.
As the main focus is on library use, research that looks at factors such as library expenditure, collection development and staffing is out of scope for the purpose of this paper. Furthermore, studies that look at library impact on teaching effectiveness, the research environment and overall institutional quality and assessment is also out of scope. As this review is concerned with student outcomes at higher education institutions, only research into use of resources at academic libraries has been considered; however, it should be noted that academic librarians can also learn from their counterparts in other types of libraries. A variety of publication types were sourced including articles, conference papers, case studies, reports and websites, which cover the experience of academic libraries in international and Australian contexts. The material reviewed surveys the various methodologies being employed in the research, such as surveys, focus groups, data from library and institution systems, and other measures which compare library usage with evaluations of student success.
At the outset it’s important to understand what is meant by use in the context of use of library resources. Fleming-May (2011) has conducted interesting research into understanding the various discursive meanings and construction of the use concept in the professional and scholarly journal literature. Whilst use is deployed in the literature as a concept with a seemingly universal meaning, in practice there is no agreement on what a use is and the concept of library use as presented in the LIS literature has ‘several separate and appreciably different facets, or meanings’ (Butkovich, 1996; Fleming-May, 2011. p.306). To clarify and illuminate its polysemic meanings and construct a typology, Fleming-May (2011, p. 301) applies Beth L. Rodgers’s Evolutionary Concept Analysis (ECA), which is ‘an approach that considers the ways in which a concept is applied within a given context in order to identify its attributes within that context.’ ECA has similar epistemological foundations to Foucauldian discourse analysis, which is a methodology commonly applied in the social sciences (Fleming-May, 2011).
In the analysis of the literature, Fleming-May (2011) suggests that library use can be organised into four categories:
use of the library as an abstraction, or general idea; use of the library as an implement, or tool; use of the library as a transaction or occurring within a discrete instance; and use of the library as a complex process (p. 306).
For the purposes of this paper we will only be concerned with use of the library as a transaction or instance. Use as an instance refers to the ‘transactional instances of the library or information that can be recorded and quantified’; for example, circulation, interlibrary loan requests, database usage, gate counts, reference questions answered, etc. Whilst this approach provides quantitative data on the transactional use of the library’s resources it doesn’t provide any qualitative examination of the user’s motivation in choosing to access those resources (Fleming-May, 2011). When discussing the transactional instances of electronic resources there are a number of challenges, so any discussion of usage of electronic resources should consider the differences in the way vendors generate reports as well as the inconsistencies in defining transactional instances such as clicking on or downloading material – both which are frequently referred to as “usage” or “use” (Fleming-May, 2011).
Directly relevant to this review, Fleming-May (2011) also notes how interest in better understanding library use has intensified in recent times ‘due to changing opinion about appropriate methods for measuring library effectiveness.’ There has been a shift away from the traditional quantifiable measures of inputs and outputs (for example, through counting transaction uses) to more qualitative approaches such as outcomes-based assessment. This is being driven at the institutional level, and as academic libraries are increasingly being tasked with demonstrating the value they provide to the university and its wider stakeholders, ‘measuring inputs and outputs has become an increasingly inadequate method of demonstrating the ways in which libraries contribute to their communities’ (Fleming-May, 2011. p. 300).
A review of the literature quickly shows that academic libraries are increasingly being faced with the challenge of proving their value to the institution and its stakeholders. Oakleaf (2010, p.7) cites how higher education providers have had to adopt corporate values and practices, which have caused an ‘internal paradox between assessment to improve academic programs and assessment for external audiences designed to answer calls for accountability from policy makers and the public.’ Driven by increasing demands for accountability, colleges and universities are under pressure from their stakeholders to prove value ‘in terms of student outcomes such as persistence, graduation, and employment, as well as student learning outcomes (Matthews, 2012; Saunders, 2015, p. 285).
This change in the higher education landscape has also disrupted the symbolic and seemingly protected status of academic libraries as the “heart of the university” (Oakleaf, 2010; Allison, 2015; Stemmer and Mahan, 2016). In the same way that higher education institutions are required to demonstrate evidence they are achieving their goals, academic libraries must also prove their value. Academic libraries can ‘no longer rely on their stakeholders’ belief in their importance’ and must now ‘demonstrate their value’. Therefore, ‘[l]ibrarians are increasingly called upon to document and articulate the value of academic and research libraries and their contribution to institutional mission and goals’ (Oakleaf, 2010, p.4). So, academic libraries must now ask the ultimate question: “How does the library advance the missions of the institution?” (Oakleaf, 2010, p.11).
In light of these changes in the landscape, library leadership has had to find data that demonstrate the value of the library in institutional terms (Stemmer and Mahan, 2016). At the 2010 Library Assessment Conference, the keynote speaker suggested to the audience that:
In this digital age you are in possession of a valuable resource, library transactions data for your student, staff and faculty patrons. That data can be used to evaluate the impact of library services and resources on outcomes of value to the university (Shulenburger, 2010, p. 4).
And as Oakleaf (2010) notes,
until libraries know that student #5 with major A has downloaded B number of articles from database C, checked out D number of books, participated in E workshops and online tutorials, and completed courses F, G, and H, libraries cannot correlate any of those student information behaviours with attainment of other outcomes. Until librarians do that, they will be blocked in many of their efforts to demonstrate value (p. 96).
With this aim in mind, Matthews (2012, p.257) suggests that it would be useful to build a data warehouse that could pull together a large array of data across various systems and silos of the institution, and from this central data repository, ‘the library would be able to prepare a wide variety of data analysis and correlations to help determine the value of library resources’. In some cases, rather than the library developing its own data warehouse, a campus data repository may already exist, so libraries should discover what other resources are available and work in partnerships with other campus departments (Matthews, 2012). To get around the issues of privacy, Oakleaf (2010) recommends that data systems should strip out individual identifiers in information records to protect the privacy of individuals.
In 2010, to understand and meet these new challenges, the Association of College and Research Libraries commissioned the report, Value of Academic Libraries: A Comprehensive Research Review and Report. The report provides a thorough review into the current state of the literature on the value of academic libraries within an institutional context and sets out a research agenda that has sparked new research in impact studies, which is assisting academic libraries better articulate their value in institutional terms to their stakeholders (Oakleaf, 2010. p. 25; Stemmer and Mahan, 2016). Based on the literature, the report also presents recommendations for how academic libraries should demonstrate value, identifies potential surrogates for library value, and suggests possible areas of correlation for the collection of library data (Oakleaf, 2010). It should be noted that the report does not provide an overview of methods for assessing library value within a library context. However, this review does provide an overview of various methodologies in the section below. The importance of the report can be seen in the frequency it is cited in the literature, and by virtue of its reference in the leading statement on “Value and Impact of University Libraries” on the website of the Council of Australian University Librarians (CAUL).
The report also suggests that ACRL create a professional development program to build the profession’s capacity to ‘document, demonstrate, and communicate library value in alignment with institutional goals’ (Brown and Malenfant, 2012, p. 4). Based on this recommendation ACRL in partnership with other professional organisations convened two summits under the auspices of the “Building Capacity for Demonstrating the Value of Academic Libraries” project. An outcome of the summits was a report titled, Connect, Collaborate, and Communicate: A Report from the Value of Academic Libraries Summit (Brown and Malenfant, 2012). The report also provided a number of important recommendations of which some are set out below.
Recommendation 1: Increase librarians’ understanding of library value and impact in relation to various dimensions of student learning and success.
The report recommends that assessment of student learning should take into consideration a number of factors including demographics, learning styles, educational goals, motivations and instructional format using a variety of qualitative and quantitative methodologies, such as surveys, testing, comparative data, interviews, etc. Based on this recommendation, the report suggests a number of actions for the library profession, which include developing a research agenda that considers the key questions raised in Oakleaf’s (2010) report; investigating how the library can increase library impact; and identifying common data sources available at the institution that can be combined with library data to document student learning and success (Brown and Malenfant, 2013, p. 12).
Recommendation 2: Articulate and promote the importance of assessment competencies necessary for documenting and communicating library impact on student learning and success.
As well as identifying a need for skills in incorporating outcomes into library planning and evaluation, and leadership in being able to lead conversation about assessment, the report highlights a need for data competencies so that librarian can apply ‘knowledge of assessment data, including the different roles of quantitative and qualitative data, sources of data, and the analysis and interpretation of data’ (Brown and Malenfant, 2013, p. 12). This is echoed by Matthews (2012, p. 257), who says that ‘being a good “data jockey” will increasingly become a real marketable skill for librarians.’
Oakleaf (2010, p. 29) points out the positive opportunities for the profession noting that ‘the current higher education environment offers librarians an opportunity to accelerate change.’ Taking this as a great opportunity to update their roles, ‘librarians can reconceptualise their expertise, skills, and roles in the context of institutional mission, not traditional library functions alone.’ Therefore, professional development of current and future librarians is necessary so that librarians can better articulate – in institutional terms – the impact and value the library has in contributing to the institutional mission.
Having understood the factors driving the need for academic libraries to demonstrate their value in institutional terms to stakeholders, and the research agenda at large, we can now look at specific examples of research into the use of library resources and student outcomes. The following research has been reviewed in context to the preceding discussion. Whilst the demand for research into the value of academic libraries that is articulated in institutional terms is relatively recent, studies of the impact of academic libraries on student outcomes can be traced further back. The need for research in this area was first articulated by Lane in 1966. In his seminal paper, “Assessing the Undergraduates’ Use of the University Library, he recognised that assessing the library in terms of physical facilities, collections and its budget, was not a sufficient ‘measure of the library’s effectiveness as an instrument of education’ (Lane, 1966. p. 277). He argued that, ‘such measures can be obtained only by assessing the extent to which students use the library and the extent to which use relates to academic growth’ (Lane, 1966. p.277). Lane also acknowledged some of the difficulties in conducting this type of research noting issues that continue to plague researchers and the methodologies employed to the present day. In particular, he notes how these types of assessment are time-consuming, expensive and difficult to achieve with complete objectivity (Lane, 1966. p. 277). However, he does note that these types of studies can produce worthwhile results for the library’s stakeholders by providing ‘information useful to administrators, students and faculty’ (Lane, 1966. p. 277).
A few years later, Kramer and Kramer (1968) published a study that investigated the connection between library use, retention and GPA scores among college freshmen at California State Polytechnic College in the United States. Library use was measured by looking at library loan records, which indicated how many books were checked out; and data that showed factors such as name, sex, major, return or non-return to school in fall 1964, and grade point average (GPA) were obtained outside the library from the registrar’s office (Kramer and Kramer, 1968). The study found that students who borrowed no books during the period achieved a lower GPA than students who used the library. Data also showed a strong indication that students who resided on-campus had a higher correlation with persistence or retention. According to Kramer and Kramer (1968, p. 312), their research appeared to show ‘a strong and statistically significant correlation between library use and student persistence.’ Based on the findings that a high proportion of students did not borrow any library books during the research period, they suggest that counseling and orientation could be productive in improving academic success and persistence (Kramer and Kramer, 1968).
Almost two decades later, Hiscock (1986) directly posed the question: “Does library usage affect academic performance?” At the time Hiscock (1986, p.207) acknowledged the lack of interest in the library by the institution, noting ‘a degree of ignorance of what really happens in libraries and an absence of research to investigate the relationship between usage of libraries and academic performance.’ The aim of Hiscock’s (1986) research was to examine whether library use affected the academic outcomes of the students surveyed in the study. Similar to Kramer and Kramer (1968), Hiscock (1986) wanted to understand whether students who used libraries performed better academically than students who did not.
Data was gathered through a survey of 196 students across selected first and second year undergraduate courses at the Underdale campus of the South Australian College of Advanced Education in Australia. The questionnaire asked questions about various types of library usage including: usage of library staff; the catalogue; resources such as encyclopaedias, indexes and abstracts; photocopying facilities; and use of the library as a place for private study (Hiscock, 1986, p. 208).
Looking at various information-seeking behaviour models the study sought to adopt an existing model ‘to aid in the construction of hypotheses to evaluate the effect of library usage on academic performance’ (Hiscock, 1986, p. 209). Hiscock (1986) arrived at nine hypotheses, which she tested using statistical methods and reported on the results for each hypothesis. Overall, the results were generally disappointing, however, she did identify two areas that were associated with positive academic performance: previous experience of using libraries and use of the library catalogue (Hiscock, 1986. p. 213).
In 1992, the search for methods for better understanding library impact on student outcomes continued with Powell’s study, “Impact Assessment of University Libraries: A Consideration of Issues and Research Methodologies.” Powell (1992, p. 249) notes that since the early 1970s, much of the interest in measuring the effectiveness of libraries has focused on performance and/or output measures, however, whilst valid measures, ‘librarians must somehow document that the use of library services and resources actually has a beneficial impact on the user.’ Powell (1992) identifies a number of problems in determining what needs to be measured and suggests that the nature of the use must first be determined. Similar to Fleming-May (2011), due to the wide variance in the literature in describing the categories of use, and reasons or purposes for library use, he identifies the difficulty of defining what is meant by use (Powell, 1992). Across the various methodologies reviewed, Powell (1992, p.253) suggests a number of methodologies that are capable for measuring impact assessment and ‘permit adequate testing of causal relationships without sacrificing too much external validity.’ In applying better methodologies, libraries will be able ‘to know how students’ use of libraries affect their academic performance’ (Powell, 1992, p.245).
Although the early studies are important and provide a foundation for understanding the emerging need for research in this area, Oakleaf’s (2010) report sets out the research agenda and provides a call to action, and in doing so, marks the start of a fervent period of growth in impact studies, which aim to assist academic libraries to better articulate their value in institutional terms to appropriate stakeholders. In 2010, around the time that Oakleaf was setting the research agenda, Haddow and Joseph published findings of their research into library use and student retention at Curtin University in Australia. The specific aims of the study were: ‘to explore if an association between library use and student retention is evident, and to investigate whether socio-economic status (SES) and age at entry are influencing factors in library use and retention’ (Haddow and Joseph, 2010, p. 234).
To achieve these aims, Haddow and Joseph (2010) analysed enrolment, demographic and library use data for students enrolled in Semester 1, 2010 at the university. Enrolment and demographic information was provided by the university’s student database and was used to identify students that were retained or had withdrawn by the end of Semester 1. Two spreadsheets were generated from the database and included data such as student ID number, postcode, address and mature age. Students that were retained or had withdrawn were identified using unique student ID numbers (Haddow and Joseph, 2010). The Library Management System provided library use data for commencing students measured at three points in the semester. Use data collected included: number of items borrowed (loans); number of logins to a library workstation (PC logins); and number of logins to the catalogue, databases, metasearch tool, and eReserve (other logins) (Haddow and Joseph, 2010). The researchers note that ethics approval was required to conduct the study with particular consideration ‘to ensure individual students were not identified or identifiable and the secure storage of data’ (Haddow and Joseph, 2010, p. 236).
Using SPSS, a statistical software programme, quantitative analyses were applied to the data. Haddow and Joseph (2010) found that regardless of whether students were retained or had withdrawn, a large proportion (64.6%) had not borrowed items from the library during the semester. In the case of library use, as indicated by PC logins or other logins, these showed higher levels of use during the semester, with 74.6% and 83.7%, respectively (Haddow and Joseph, 2010). When all three types of library use were analysed against retention it was found that ‘retained students showed higher levels of loans, PC logins, and other logins’ (Haddow and Joseph, 2010, p. 238). In terms of demographics, the study found little differences in library use in relation to loans and other logins. However, significant differences were found for PC logins for students from low to medium SES backgrounds; and surprisingly, students from the high SES group showed no or low use of library workstations (Haddow and Joseph, 2010, p. 239). When it came to mature age students, the results showed statistically significant differences in the number of loans between mature age students and those under 21 years, with mature age students borrowing books at higher rates than younger students (Haddow and Joseph, 2010, p. 239). Due to the apparent association between library use and student retention, Haddow and Joseph (2010, p. 240) suggest there are ‘implications for the planning of orientation and information literacy activities.’
Around the same time research was being conducted in Australia, Goodall and Pattern (2011) published a case study from research that was in progress at Huddersfield University in the North of England. Librarians at the university had identified ‘a historical correlation between library usage and degree classification’, which on a priori assumptions suggested that students who borrowed more books and accessed more electronic resources achieved better grades (Goodall and Pattern, 2011. p. 160). Preliminary research showed that some student groups were not using library facilities and resources as much as was expected (Goodall and Pattern, 2011). Three sets of data were collected on use of library resources: use of electronic resources, book loans, and visits to the library. These variables were then graphed, which showed ‘consistent amounts of no and low use at campus, academic school, degree-type and course level’ (Goodall and Pattern, 2011. p. 159). When these results were combined with data showing academic achievement it raised the question of whether there was a positive correlation between library use and student attainment.
This research opened up a new area of interest in impact studies, because at the time, whilst there had been previous studies that looked at linking grades and retention to use of library resources in investigating the impact of the library on student outcomes, engagement of non-users was relatively unchartered territory (Goodall and Pattern, 2011, p. 162). In drawing attention to the importance of understanding non/low use of the library, Goodall and Pattern (2011, p. 162) identified it as ‘a central issue for individual students concerned about their grades, for academic staff concerned about attainment, and for institutions concerned about retention.’ In this way, the researchers hoped that if they could understand the reasons behind non/low use then effective interventions could be developed and trialled, and strategies could be implemented that would improve ‘the grades of all students, from the bottom up, rather than just continuing to support those which are already high flyers’ (Goodall and Pattern, 2011, p. 160).
The findings of this research were presented at the 2010 UKSG Conference in Edinburgh, which attracted interest from other universities who were interested in benchmarking. However, at the time is was suggested that the data still had not been tested for statistical significance; therefore, it was unknown whether the findings at Huddersfield were due to the sample data used, rather than a true reflection that possibly existed across a wider population (Stone and Ramsden, 2013). Based on the initial research the project was expanded across eight universities in the United Kingdom. The Library Impact Data Project (LIDP) was a six-month project funded by Jisc to investigate the hypothesis that: “There is a statistically significant correlation across a number of universities between library activity data and student attainment” (Stone and Ramsden, 2013. p. 546). The LIDP looked at usage data of 33,074 undergraduate students across the participating universities with e-resources usage, borrowing statistics and gate counts measured against final degree award. By supporting the hypothesis, the LIDP aimed, ‘to give a greater understanding of the link between library activity data and student attainment, which would show a tangible benefit to the higher education (HE) community’ (Stone and Ramsden, 2011. p. 550).
In line with Oakleaf (2010), the project was concerned with data protection issues, which were seen as a potential risk. Due to the sensitive nature it was important that data was obtained in a way that met legal and university regulations and students were informed that their library use may be measured. The data was also fully anonymised and made available to the project as part of an open data agreement and any courses that only had a few students were excluded from the data to prevent identification (Stone and Ramsden, 2013).
The researchers used both qualitative and quantitative methodologies in the project. For example, qualitative data was collected using focus groups and followed up with a brief questionnaire, which helped to qualify issues that were identified. The transcripts were examined for any apparent themes and statements were coded (Stone and Ramsden, 2013). Quantitative data were analysed with statistical methods, which showed a positive relationship between use of e-resources and degree result; book borrowing and degree result; but not between gate counts and degree result. The data suggested that the more an e-resource or book is used, ‘the more likely a student is to have attained a higher-level degree result’ (Stone and Ramsden, 2013, p. 554).
Whilst the project was regarded as successful in demonstrating a statistically significant relationship between use of library resources and final degree award and thereby substantiating the initial hypothesis, researchers also identified a number of issues similar to those discussed above. In terms of data reliability there are inherent issues when it comes to use data. For example, data for use of e-resources and borrowing of books does not reveal whether the item has actually been read, understood and referenced, and in the case of e-resources, counting clicks and downloads is problematic and variable across different databases, so heavy usage does not necessarily relate to high information-seeking or academic skills. The project also found that it underestimated the time taken to analyse the data with collection and analysis taking four out of the six months of the project (Stone and Ramsden, 2013).
In December 2011, the project secured another tranche of funding to extend the LIDP into phase II, which as per Oakleaf (2010) looked at demographic factors such as gender, age, ethnicity, and country of origin to further enrich the quality of data to identify additional causal links (Stone and Ramsden, 2013). Research in the U.K. has continued and the project has since expanded into a partnership between Jisc, Mimas (at the University of Manchester) and the University of Huddersfield, and is now named the Library Analytics and Metrics Project (LAMP).
Another recent example of research combining library transaction data with student performance data was undertaken by the University of Wollongong Library (UWL) in Australia. Like many libraries around the world, the library has used client satisfactions surveys to collect feedback from its users with information gained used to drive continuous improvement to the quality of services (Jantti and Cox, 2012). However, whilst useful, the researchers argue that there are significant limitations to surveys, including
they are naturally biased towards library users; they are not run frequently enough to support marketing; and they do not measure the impact of the library on client’s success, only respondents’ subjective assessment of value and performance (Jantti and Cox, 2012, p. 69).
With these limitations in mind, UWL undertook a project in conjunction with the university’s Performance Indicator Unit to develop a data warehouse and reporting function (a Cube) that combines library usage of electronic resources with students’ demographic and academic performance data (Jantti and Cox 2012; Pepper and Jantti, 2014). The aim of the project was to help the library, ‘improve the impact of its resources and teaching activities with respect to student academic performance, and student engagement’ (Jantti and Cox, 2012, p. 69). And in line with much of the discussion above, ‘unambiguously demonstrate the contribution [the library] is making to institutional learning, teaching and research endeavours’ (Jantti and Cox, 2012, p. 69).
The Value Cube is a dataset that is structured around academic teaching sessions used to assess the impact of library resources on student outcomes measured by Weighted Average Marks (WAM). Note that GPA scores are generally not used in Australia with most tertiary institutions using WAM for academic grading. The Value Cube also allows the library to review demographics by level of usage giving much more granular analysis from the data set than has been achievable in previous studies (Jantti and Cox, 2012). Data for the Library Cube was pulled from the Library Management System, which included loans data and usage data for electronic resources with ezproxy logs used to determine which databases, ebooks and ereadings materials were being used by which student (Jantti and Cox, 2012).
As with the LIDP project (Stone and Ramsden, 2013), the researchers observed the same limitation with usage, noting that ‘just because someone borrowed a book does not mean they read, understood or used the book’ (Jantti and Cox, 2012). Furthermore, the issue of correlation and cause was raised as there could be many other factors that help contribute to students’ academic performance (Jantti and Cox, 2012). However, the project did find that there was very strong evidence that the library was positively impacting on students’ academic success; for example, the researchers found that students who used the library’s collection – through borrowing books and using electronic resources – were more likely to achieve higher WAMs (Jantti and Cox, 2012).
Having been able to see that the library was indeed providing value to students who used library resources, the next phase of the project sought to answer further questions and solve problems around which students were using resources; assist with interventions in conjunction with faculty to encourage non/low users to engage with resources; and more ambitiously, to test if the library had been successful in influencing behaviour by looking at post-intervention data (Pepper and Jantti, 2014). To assist in these new endeavours, a Marketing Cube was built, which replicated the demographic elements of the Value Cube and contained information on which specific databases were being accessed on a weekly basis to provide a more immediate view of resource use, which is allowing the library to understand the context in which library resources are being used in relation to information need (Pepper and Jantti, 2014).
This paper has provided an overview of the state of research into the value of academic libraries and impact studies in higher education. Due to the increased demands of accountability, academic libraries are challenged with demonstrating the value they provide to the university and its wider stakeholders. This shift in the higher education landscape has a necessitated a shift in the need for impact studies to go beyond impact, so that libraries can document and articulate the value they contribute to the institutional mission and goals. This need has accelerated the research agenda and produced a growing body of research that specifically investigates the links between use of library resources and student outcomes. All of the studies that were reviewed indicated at least some correlation between library use and academic achievement, however, correlation does not necessarily mean cause. The research is also challenged by the difficulties around the meaning of use and the inconsistencies in methodologies, as well as the inherent issue around use data itself – the data doesn’t reveal that library resources such as books have actually been read, understood or referenced – and when it comes to e-resources there is variability across platforms and vendors in how usage is counted. To better achieve the aims of future research in this important area of LIS studies, librarians and researchers will need to take heed of Oakleaf’s recommendations for professional development. Librarians who have the necessary data competencies will be able to design better research that demonstrates the value their library is making in institutional terms. Ultimately, it is these librarians of the future who will be able to go beyond their traditional library functions and take advantage of the challenges and changes in the higher education landscape, and lead conversations by confidently responding to the questions set forth by the research agenda.
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