Article
Assessing the Library’s Influence on Freshman and
Senior Level Outcomes with User Surveys
John K. Stemmer
Director of Library Services
Bellarmine University
Louisville, Kentucky, United
States of America
Email: jstemmer@bellarmine.edu
David M. Mahan
Executive Director of
Institutional Research and Assessment
Manhattan College
Riverdale, New York, United
States of America
Email: david.mahan@manhattan.edu
Received: 14 Feb. 2015 Accepted:
30 Apr. 2015
2015 Stemmer and Mahan. This is an Open
Access article distributed under the terms of the Creative Commons‐Attribution‐Noncommercial‐Share Alike License 4.0
International (http://creativecommons.org/licenses/by-nc-sa/4.0/),
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.
Abstract
Objectives
–
This study seeks to identify areas where relationships exist between a
student’s library usage and student outcomes at Bellarmine University, a
private master’s level institution. The study has two primary aims. The first
is to see if an operationally oriented user survey can be used to provide
evidence of the library’s support for institutionally important student
outcomes. The second is to develop a regression model that provides a big
picture with multiple variables to determine if library factors are still
significant in student outcomes when controlling for significant demographic
factors.
Methods
–
The library regularly conducts student user surveys, and this study examines
the results of the first three surveys, from 2007, 2008 and 2010. These surveys
include individually identifiable data on why students come to the library and
how often they use it in person and online. Researchers aggregated student
responses into class-based cohorts and used regression analysis to analyze the
extent and significance of the relationships, if any, that exist between
student use of the library and student outcomes such as retention, graduation
and cumulative GPA. The study takes into consideration known significant
student demographic factors such as American Collect Testing (ACT) composite
score, full- or part-time status, and their session GPA.
Results
–
The study identifies specific library services and resources that have
significant correlations with the selected student learning measures and outcomes.
For freshman students, the ability to access the library online influences both
retention and graduation. In looking at freshman learning outcomes represented
by GPA, the results again indicate that the library has a positive influence on
a student’s GPA. The library’s influence appears through two factors that
highlight the library as a place: providing a place to study alone and as a
place that has specialized equipment available to students. The library
influences seniors’ cumulative GPA differently than for freshmen, primarily
through the library’s role as an information resource. The variable check out
books had a positive impact on senior’s GPA.
Conclusions
–
This study indicates that the library does have an influence on student
outcomes, whether learning outcomes, represented by cumulative GPA, or more
typical student success outcomes, represented by second-year retention and
graduation. This is true even when controlling for certain demographics,
including the student’s ACT score, whether the student is part-time or
full-time, and their session GPA. The factors that influence an individual
student’s outcome change depending on the point in time in the undergraduate
experience. These statistical analyses provide significant evidence for the value
the library provides in support of institutionally important student outcome
goals.
Introduction
At Bellarmine University the administration is
creating a culture of assessment. As part of this effort, Bellarmine University
Library conducts user surveys of faculty or students as a matter of course,
starting in 2007. These surveys are a direct result of the need to provide
assessment measures to outside stakeholders, in this case for the Southern
Association of Colleges and Schools accreditation review. They are also used as
a focused supplement to benchmark studies that had already been conducted. In many
ways this practice reflects the state of traditional library assessment, in
that it is generally input/output oriented and, more recently, operationally
oriented. In both of these assessment efforts, benchmarks of inputs/outputs or
user satisfaction surveys, whether locally or nationally (e.g., LibQUAL), the
assessment has remained focused on the library and its internal operations.
They do not provide particularly compelling evidence for other interested
parties.
The library’s satisfaction survey is used to gauge the
effectiveness of the library’s operations as seen by its users. Traditional
library assessment has little correlation with the outcomes and positive work
of the institution; rather it provides data that indicates strengths and
weaknesses on the library’s part that the library administration can then take
efforts to further develop or correct as the case may be. Operationally these
surveys can be helpful to a library administration; they provide valuable data
that the library and its resources and services are being used and even
appreciated. However, they do not demonstrate to institutional stakeholders the
impact of the library on institutional goals and objectives.
Increasingly, academic libraries are being asked to
provide data and assessments that demonstrate the library’s connection with the
institution’s desired outcomes. They are facing increasing demands for
accountability to a broader audience than ever before. Libraries have moved
from being the only information resource to being a quality information
resource. In the current economic climate, parent institutions seek to save
every dollar they can and face difficult budget choices, often including
decreased library budgets. Meanwhile, the political climate calls for increased
accountability and greater connections between the university’s efforts and
student learning and student success. This is the changing environment faced by
higher education institutions and therefore academic libraries.
In response, librarians are more frequently seeking to
identify areas where they can demonstrate a relationship exists between a
student’s library interactions and student outcomes, in contexts relevant to a
wider audience at the university and beyond. To accomplish this Oakleaf (2010)
indicates that the library must link its data to individual students. Matthews
(2012) has likewise recently noted that success in efforts to link the library
and student outcomes will require the use of individual student data. One of
the reasons that student unit data is such a constant issue in these efforts by
librarians is that generally libraries do not track what resources are used by
which students, or how students use library resources and services. As a matter
of professional ethics, personally identifiable information is often deleted or
not even collected to maintain an individual’s privacy. So while offices of
institutional research have access to large amounts of individually
identifiable outcome data, most libraries do not.
The need for library assessment efforts that show the
influence of the library in terms of institutionally relevant outcomes, such as
student learning and student success, is highlighted in the ACRL report The Value of Academic Libraries (Oakleaf,
2010). Much work and many attempts have been made to demonstrate the library’s
direct impact on student learning and other student outcomes. Recent work
includes studies by Wong and her colleagues to examine the connections between
library material usage and student GPA (Wong & Webb, 2011), and library
instruction and graduating GPA (Wong & Cmor, 2011). Emmons and Wilkinson
(2011) examine the library’s impact on student persistence. Tenopir (2012)
reports on multiple methods being used to try and measure library value in her
status report on the lib-value project. More thought and effort has been put
forth to developing appropriate assessment measures that address the library’s
impact on students, particularly the impact on student outcomes and student
learning. Oakleaf (2011) addresses the challenges librarians face in trying to
assess the library’s connection with student learning and outcomes. Rodriguez
(2011) reports on his efforts to develop one such tool, the protocol for
Understanding Library Impacts, to show library impact on student learning, and
his first results are promising (Rodriguez, 2012). In the UK, the Library
Impact Data Project has been examining the issue of developing appropriate
measures to assess the library’s role in student learning and outcomes, finding
significant correlation between library usage and student attainment of the
final degree (Stone, Pattern, & Ramsden).
Aims
This exploratory study seeks to identify areas where
relationships exist between a student’s library usage and relevant student
outcomes at Bellarmine University. The two primary aims are to see if an
operationally oriented user survey can be used to provide evidence of the
library’s role in institutionally important student outcomes, and to move
beyond the simple, one to one, correlations and use a regression model that
provides a bigger picture with multiple variables to determine if, when
significant demographic factors are controlled, library factors are still
significant in student outcomes.
Methods
Like many academic libraries, Bellarmine University
library uses a student user survey as part of its assessment efforts. Between
2007 and 2010 it conducted three such surveys, and now does one every other
year. These surveys provide data on why students come to the library and how
often they use the library. The library survey asked questions in two groupings
(see Appendix A). The first grouping asks the reasons that students come to the
library, with a list of 18 possible reasons with checkboxes. Respondents were
asked to check all reasons that applied. The second group of questions requests
information on how often a student came to the library and how often the
student used the library online, with response options ranging from daily to
never. Most importantly, the survey is not anonymous. The library can identify
how students report using the library at an individual level.
Researchers paired the library survey data with the
university’s Office of Institutional Research student data to first determine
if there were any significant relationships between a student’s self-reported
library usage and known student outcomes. They used a logistic regression of
all the library input variables against selected student outcomes: retention,
graduation, and GPA. They also determined that the data would be viewed in
undergraduate class-based cohorts to minimize variations among the respondent
experiences. In addition to the library variables, researchers tested a number
of demographic factors drawn from institutional research data against selected
student outcomes to identify the most significant demographic factors for the
individual cohorts. Using these control factors and the library variables the
researchers developed three research questions:
Undergraduate students participated in library surveys
in the years 2007, 2008 and 2010. Bellarmine University is a small, private,
Catholic university located in Louisville, Kentucky. Total university
enrollment is approximately 2,000 undergraduate students and nearly 800 graduate
students. There were over 1,000 students living in the residence halls. The
institution currently offers over 50 undergraduate bachelor degree programs and
over 20 graduate programs. Over 80% of Bellarmine’s undergraduate students
attend full-time and are under 25 years of age.
Bellarmine University has an incoming class of about
600 students every fall. The following demographic and academic preparedness
measures are consistent across freshman cohorts:
Due to these similar demographics across the student
body, researchers collapsed the survey responses for 2007, 2008, and 2010 and
studied the data as class-based cohorts.
Due to the limited number of undergraduate students at
the institution, sampling was unnecessary because the entire population could
easily be requested to participate. On designated years, the institution
invited the undergraduate populations to participate in the library survey.
Most members of the freshmen and senior populations start at the university as
first-time, traditional age freshmen; however, all freshman and senior students
were invited to participate in the survey, regardless of whether they enrolled
as traditional freshmen, transfer students, or re-admitted students. The response
rate for the three surveys ranged from a low of 20% to a high of 26%.
Results
The variables considered, Full-time/Part-time Status, Session
GPA, Race, and ACT composite score, either singly or in combinations,
were identified by a logistic regression as having a significant relationship
to outcome variables, whether a student graduated or the cumulative GPA (p<.05). These variables were
controlled in our analyses to consider whether library usage variables
independently influenced student outcomes (see Appendix B).
Analysis One: Second-year Retention of Freshman
Students
Researchers conducted a forward entry logistic
regression using freshmen student data only (n=370), considering any
significant library variables related to outcome variable, retained in a
students’ second year. There were 336 freshmen returning in the second year
(91%) and 34 students did not (9%). The omnibus test of model coefficients was
significant, with a chi-square test result of 8.227 (p<.05). One library variable was determined to be a significant
positive predictor of returning for the second year: Access library online (see Table 1). The Nagelkerke R-squared was
0.048.
Researchers conducted second
logistic regression in two steps. First, by identifying the control variables Full-time/Part-time Status, ACT composite score,
and Session GPA, which were selected
because correlational analysis revealed significance relationship to Return (p<.05). Second, they used a forward entry technique to consider library
variables after the control variables. The omnibus test of model coefficients
was significant, with a resulting chi-square of 31.021 (df=3, p<.001). They found that Session GPA significantly predicted Return. After the control variables were
entered, the library variable Access library
online again significantly predicted whether a student returned in their
second year using the same forward entry method (p<.05) (see Table 2). The Nagelkerke R-squared was 0.206.
Table 1
Significant Library
Variables Related to Retention
|
|
|||||||||
Measure |
B |
S.E. |
Wald |
Df |
Sig. |
Exp(B) |
|
|
|
|
Access library online |
.432 |
.158 |
7.504 |
1 |
.006 |
1.54 |
|
|
|
|
Constant |
1.242 |
.388 |
10.236 |
1 |
.001 |
3.463 |
|
|
|
|
Table 2
Significant Control and
Library Variables Related to Retention
|
|
|
|
||||
|
B |
S.E. |
Wald |
Df |
Sig. |
Exp(B) |
|
FT or PT |
21.868 |
40192.7 |
.000 |
1 |
1 |
.000 |
|
ACT |
.074 |
.067 |
1.238 |
1 |
.266 |
1.077 |
|
Session GPA |
.998 |
.224 |
19.859 |
1 |
.000 |
2.712 |
|
Access library online |
.311 |
.157 |
3.947 |
1 |
.047 |
1.365 |
|
Constant |
18.721 |
40192.7 |
000 |
1 |
1 |
1.4E+08 |
|
Table 3
Significant library
variables related to graduation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp (B) |
Access Library Online |
.245 |
.117 |
4.411 |
1 |
.036 |
1.277 |
Constant |
.240 |
.335 |
.513 |
1 |
.474 |
1.271 |
Analysis Two: Graduation of Freshman Students from the
Same Institution
Researchers conducted a forward entry logistic regression using freshmen student data only
(n=220, after combining freshmen from surveys in 2007 and 2008), considering
any library variables related to outcome variable to identify any significant
library variables related to graduation. This included respondents who graduated
within four years for 2008 freshman surveys or within five years for 2007
freshman surveys. Respondents were
predominantly traditional-aged freshmen, and primarily full-time students.
There were 156 freshmen that graduated (71%) and 64 students did not (29%). The
omnibus test of model coefficients was significant, with a resulting chi-square
of 4.584 (p<.05). Researchers
found that one library variable, Access
library online (p<.05), was a
significant predictor of graduation It
was a positive predictor, and confirmation of this was associated with students
graduating (see Table 3). The Nagelkerke R-squared was .029.
Researchers conducted a
second logistic regression in two steps. First using the following control
variables: Full-time/Part-time status,
ACT score, Session GPA, and Race,
which were selected because correlational analysis revealed significance
relationship to Graduate (p<.05).
Second, using a forward entry technique to consider library variables after
including the demographic control variables. The omnibus test of model
coefficients was significant, with a chi-square result of 37.943 (p<.001). After entering the control
variables, the library variable Access library
online still significantly predicted whether a student graduated, using the
forward entry method (p<.05) (see
Table 4). The Nagelkerke R-squared was 0.255.
Analysis Three: First-year Cumulative GPA
Researchers conducted an
analysis of freshman student data only (n=370), considering any significant
variables correlated with the outcome variable Cumulative GPA. They employed a forward entry ordinary least
squares (OLS) regression to consider what variables, if any, predict Cumulative GPA. Researchers identified
two significant library variables, Study
alone and Use of printer or
photocopier, as positive predictors, for agreement with them was associated
with a higher Cumulative GPA. The
adjusted R-squared was .030. Controlling for the significant non-library
variable, ACT Composite, entering it
in the model before library survey items, the R-squared is .15 for the ACT
variable; however Study alone and Use of printer and photocopier still
present themselves as significant factors for Cumulative GPA (see Tables 5 and 6).
Table 4
Significant
Control and Library Variables Related to Graduation
|
B |
S.E. |
Wald |
df |
Sig. |
Exp(B) |
||
FT or PT |
22.999 |
40193.2 |
.000 |
1 |
1 |
9.74E+09 |
||
ACT |
0.065 |
0.054 |
1.44 |
1 |
0.23 |
1.067 |
||
Session GPA |
1.09 |
0.282 |
14.892 |
1 |
0 |
2.974 |
||
Race |
0.954 |
0.437 |
4.766 |
1 |
0.029 |
2.595 |
||
Access library online |
0.262 |
0.131 |
3.986 |
1 |
0.046 |
1.299 |
||
Constant |
-28.586 |
40193.2 |
.000 |
1 |
0.999 |
.000 |
||
Table 5
Control
and Library Variables Related to First-year GPA
Std. Error of the Estimate |
Change Statistics |
||||||||
Model |
R |
R Square |
Adjusted R Square |
R Square Change |
F Change |
df1 |
df2 |
Sig. F Change |
|
ACT |
0.396 |
0.157 |
0.154 |
0.561 |
0.157 |
67.240 |
1 |
362 |
0.000 |
ACT, study alone |
0.426 |
0.182 |
0.177 |
0.553 |
0.025 |
11.014 |
1 |
361 |
0.001 |
ACT, study alone, use
printer or photocopier |
0.437 |
0.191 |
0.184 |
0.551 |
0.009 |
4.005 |
1 |
360 |
0.046 |
Additional library survey
items were not significant in the model, but had significant positive
correlation with students’ cumulative GPA at the end of the their freshman year
(p<.01):
·
Access library online
·
Use computer for academic
purposes
·
Consult a reference
librarian
Analysis Four: Senior Cumulative GPA
Researchers conducted an
analysis of senior student data only (n=360), considering any significant
library variables correlated with the outcome variable Cumulative GPA. Researchers
used a forward entry ordinary least squares (OLS) regression to consider what
variables, if any, predict Cumulative GPA.
They identified two significant library predictors: Check out books and Use of
group study rooms. Check out books
was a positive predictor, and confirmation of this was associated with a higher
Cumulative GPA. Use of group study rooms was a negative predictor. The adjusted R-squared was .045. When
the control variable ACT Composite is
entered in the model before library survey items, the R-squared is .275 for the
ACT variable; however while Check out
books still presents as significant, Use
of group study room is no longer a significant factor (See Tables 7 and 8).
Table 6
First-year GPA Model
Coefficients
Unstandardized
Coefficients |
Standardized Coefficients |
||||
Model |
B |
Std. Error |
Beta |
t |
Sig. |
(Constant) |
1.443 |
0.218 |
6.617 |
0.000 |
|
ACT |
0.071 |
0.009 |
0.396 |
8.200 |
0.000 |
(Constant) |
1.243 |
0.223 |
5.565 |
0.000 |
|
ACT |
0.070 |
0.009 |
0.391 |
8.198 |
0.000 |
study alone |
0.265 |
0.080 |
0.158 |
3.319 |
0.001 |
(Constant) |
1.201 |
0.224 |
5.372 |
0.000 |
|
ACT |
0.069 |
0.008 |
0.385 |
8.099 |
0.000 |
study alone |
0.231 |
0.081 |
0.138 |
2.845 |
0.005 |
use printer or photocopier |
0.133 |
0.066 |
0.097 |
2.001 |
0.046 |
Table 7
Control and Library
Variables Related to Senior GPA
Std. Error of the Estimate |
Change Statistics |
||||||||
Model |
R |
R Square |
Adjusted R Square |
R Square Change |
F Change |
df1 |
df2 |
Sig. F Change |
|
ACT |
0.525 |
0.275 |
0.273 |
0.353 |
0.275 |
104.147 |
1 |
274 |
0.000 |
ACT, check out books |
0.540 |
0.292 |
0.287 |
0.350 |
0.016 |
6.333 |
1 |
273 |
0.012 |
Table 8
Senior
Year Cumulative GPA Model Coefficients
Unstandardized
Coefficients |
Standardized Coefficients |
||||
Model |
B |
Std. Error |
Beta |
t |
Sig. |
(Constant) |
1.780 |
0.155 |
11.456 |
0.000 |
|
ACT |
0.063 |
0.006 |
0.525 |
10.205 |
0.000 |
(Constant) |
1.743 |
0.155 |
11.276 |
0.000 |
|
ACT |
0.062 |
0.006 |
0.513 |
10.029 |
0.000 |
Check out books |
0.111 |
0.044 |
0.129 |
2.516 |
0.012 |
Discussion
The analysis indicates that
student usage of the library manifests itself in a number of ways, including
use as an academic or information resource, use as an information service, as a
place to study alone or in a group, a place to use equipment available in the
library, and for social reasons. Many of these have previously appeared in the
library literature as recognizable parts of a library’s offerings to the
university community. The results illustrate that library factors consistently
show a significant relationship to student outcome variables, even when control
factors are considered.
The study’s findings confirm
other studies that link the use of library services and resources to a
student’s learning and success outcomes. Wong and Webb (2011) identified a
single aspect of library usage (checking out books) that correlates with a
student outcome (GPA). For seniors, checking out books was a significant
predictor in the model for their GPA. Emmons and Wilkinson (2011) identify that
the ratio of professional staff to full-time students has an impact on student
persistence. This study also found that the library has an influence on
freshman retention and graduation, through the use of online resources.
In examining the library’s
influence on student success outcomes of second-year retention and graduation,
this study indicates that the library’s support does favourably influence
student success. In looking at second-year retention of freshmen, accessing the
library online was identified as the most significant library factor and even
after controlling for significant demographic variables, this factor was still
identified by the model as significant influence on retention. For freshman
students the same factor influenced both retention and graduation: the
availability of online access to the library. Freshman students that accessed
the library online more frequently were more likely to return for their second
year and to graduate. Using the library’s academic information resources
appears to favourably influence a freshman’s student success outcomes.
In looking at student
learning outcomes represented by GPA, the results again indicate that the
library does have a consistent and positive influence on a student’s GPA, though
specific library factors change depending on where in their academic career the
student is located. For first-year students the library’s influence appears
through two variables that highlight the library as a place: providing a place
to study alone and as a place that has specialized equipment available to
students. In a sense, as new students they are getting acclimated to the place
of the library in their academic efforts. Interestingly, the impact of studying
alone repeats a finding by Arum and Roska (2011) showing that time spent
studying alone is more academically beneficial than time spent studying in
groups. Seniors cumulative GPA was influenced by the library differently than
first-year students. Checking out books
had a positive impact on senior’s GPA. Interestingly Using group study rooms, which had a negative effect when
considering the library variables alone, was not significant when control
variables were entered. The consistent presentation of library variables after
the use of significant control variables shows that library factors do have
significant connections to student outcomes and success.
Limitations
Researchers employed a
number of different non-library control variables in this study, but there are
many that were not considered. The researchers wanted a “bigger picture” view
of the library’s relationship with student outcomes more than a one-to-one
correlation, but this is not intended to imply causation. It should be noted
that this study relies on self-reported data, the value of which has recently
been challenged (Porter, 2011). It is also worth noting that this study focuses
on acquiring factual information on specific activities of the students. This
study does not seek information on students’ perception of ability or growth
over time, merely what students were doing the year the survey was taken. It
was designed within the parameters outlined by Gonyea (2005) as an appropriate
means of creating and using self-reported data.
Conclusion
Not surprisingly, not all
correlations identifying the library’s influence on student outcomes were found
to be significant when using regression analysis to further examine the
relationship in conjunction with non-library control variables. The study
confirms previous correlation studies that identify the library as contributing
to student outcomes, and indicates that the library’s relationship with student
outcomes is not eliminated by control variables. The library has an influence
on first-year and senior student outcomes but does not affect them in the same
manner. The study indicates that the library provides support for students in a
number of ways: as a resource, through services, and as a place. All of these
aspects of the library’s operations are shown to be influential on student
outcomes, but no single aspect is consistent from year to year as an individual
students progress through their studies. Rather, as individual students develop
they seem to rely on different aspects of the library offerings and their use
of the library in order to succeed.
This study indicates that
the library does have an influence on freshman and senior level outcomes,
whether expressed as learning outcomes represented by cumulative GPA, or more
typical student success outcomes, such as retention and graduation rates. This
is true even when controlling for certain demographic characteristics,
including the student’s ACT score, whether the student is part-time or
full-time, and their session GPA. The factors that impact a student’s outcomes
changes depending on where in their academic career a student is located. These
statistical analyses provide significant evidence for the value provided by the
library in support of institutionally important student outcome goals.
Additional study into what students identify as different library factors over
time would be useful. Another area for further research would be to consider if
there are any differences in library usage factors based on the student’s field
of study.
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conference, Anaheim, CA. http://libvalue.cci.utk.edu/sites/default/files/ARL%20ALATenopir20126%2022%20REV%20Final.pdf
Wong, S. H. R., & Cmor, D. (2011).
Measuring association between library instruction and graduation GPA. College
& Research Libraries, 72(5), 464-473. doi: http://dx.doi.org/10.5860/crl-151
Wong, S. H. R., & Webb, T. D. (2011).
Uncovering meaningful correlation between student academic performance and
library material usage. College &
Research Libraries, 72(4), 361-370. http://dx.doi.org/10.5860/crl-129
Appendix A
2010 Library Usage Survey
Reasons you come to the library (check all that
apply):
|
|
If you never use the
library, why don't you?
Please rate the following:
|
|
|
|
|
|
|
On average, how often do
you use the library in person? |
Daily |
2 to 4 times a week |
Once a week |
2 to 3 times a month |
Once a month or less |
Never |
|
|
|
|
|
|
|
On average, how often do
you access library materials, services and databases (such as ProQuest and
EBSCOhost) without visiting the library? |
Daily |
2 to 4 times a week |
Once a week |
2 to 3 times a month |
Once a month or less |
Never |
|
|
|
|
|
|
|
Appendix B
Variables considered
Demographic and Academic
Preparedness
Library Usage Variables
Outcome Variables