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Laura Wright
Valdosta State University
 Library Instruction
 Builds a foundation of research skills
 Builds information literacy skills
 Improves confidence and research ability
 How do you convince skeptics that library instruction is valuable?
 How do you market to professors who do not include library instruction in their
courses?
 Speak their language!
 Colleges and universities are exploring ways to improve student outcomes, including
student success and retention (Matthews, 2012; Murray, Ireland, & Hackathorn,
2016).
 Colleges and universities are looking for data-based methods to:
 Identify at-risk students
 Improve student outcomes: grade point average (GPA), retention (Whalen, Saunders, &
Shelley, 2009)
 Library instruction could serve as a risk factor. Risk factors indicate when a student
is at higher risk of leaving or having a lower GPA.
 Library instruction could serve as a intervention. Interventions are designed to help
at-risk students succeed.
 Can we do a statistical analysis that shows library instruction supports GPA and
retention?
 Several researchers have investigated the relationship between library instruction and
retention or GPA in recent years.
 Attending a required, but not strictly enforced, library instruction session has been used as a
predictor for retention (Gammell, Allen, & Banach, 2012).
 Wong & Cmor found that the frequency of attending library instruction is positively
associated with student achievement (2011).
 Research on the relationship of library instruction and workshops on retention and GPA is not
consistent (Soria, Fransen, & Nackerud, 2013; Soria, Fransen, & Nackerud, 2014).
 Building on our existing culture
 Existing statistics
 Number of attendees at library
instruction
 Existing Assessment
 Student Opinion of Library
Instruction
 Minute papers
 Faculty Survey after Library
Instruction
 Library statistics
 Door count
 Number of books circulated
 Interlibrary Loan and GIL Express
 Electronic Resources
 Technology statistics
 Computer usage
 Printer usage
 Administration
 Librarians
 Data Warehouse
 Professors
 Why did we decide to collect LI attendance at in-person classes?
 Simple, easy to implement.
 Need LI attendance data if want to examine relationship between library instruction and
GPA or retention.
 Meeting with Data Warehouse
 Help plan how to collect LI attendance data.
 They collect lots of other data as a matter of course (GPA, retention, etc.).
 How does data collection affect librarians teaching LI?
 Added a step to teaching LI.
 How does data collection affect professors?
 Minimal impact – most already collect attendance.
 Workflow
 Schedule LI
 Prepare print rosters from data
warehouse widget
 Collect attendance
 Enter attendance in widget
 File rosters
 Challenges & Lessons Learned
 Lost data
 Checking accuracy
 Repeat LIs – you get what you ask for
 My dissertation examines the following relationships at Odum Library at Valdosta
State University (VSU):
 The relationship between library instruction and first-time, full-time freshmen
retention.
 The relationship between library instruction and first-year cumulative grade point
average (GPA).
 The relationship between GPA and first-time, full-time freshmen retention.
 Institutional Data is collected from VSU’s Data Warehouse
 Fall 2015 through Spring 2018
 Independent Variables
 Attending library instruction
 GPA
 Dependent Variables
 GPA
 First-time, full-time freshmen retention
 Control Variables
 Gender
 Race/ethnicity
 Pell Grant status (socioeconomic status)
 Library instruction is positively associated with retention.
 Chi-squared Test and binomial logistic regression.
 Of the students who were retained, 72.60% did attend library instruction and
64.80% did not attend library instruction (p < 0.001).
 If a student attended library instruction, they are 59% more likely to be retained
(Odds ratio (OR) =1.419, p<0.001) than students who did not attend library
instruction.
 The model only accounts for 2.6% of the variation in retention.
Is there a statistically significant relationship between
attending library instruction and first-time, full-time freshman
retention?
 Library instruction is positively associated with GPA.
 Multiple linear regression.
 Overall R-square of 0.112, indicating that 11.2% of the variation in GPA is
explained by the model.
 Gender accounts for 4.2% of the variability observed in GPA
 Pell Grant status accounts for an additional 2.5%
 Library instruction attendance accounts for an additional 0.6%
 Race/ethnicity accounts for an additional 3.9%
 Library instruction attendance accounts for only 0.6% of the variation in the
model.
Is there a statistically significant relationship between
attending library instruction and GPA?
 Library instruction has a very small positive association with retention.
 Students who attend library instruction are 59% more likely to be retained.
 Model accounts for 2.6% of the variation in retention.
 Library instruction has a very small positive association with GPA.
 Attending library instruction accounts for 0.6% of the variation observed in GPA.
 Size of the two relationships is consistent with previous research that found
positive associations.
 Cannot easily generalize or compare institutions because library instruction
varies in its format and delivery.
 GPA is positively associated with retention.
 Binomial logistic regression.
 If a student has a higher GPA, they are 77% more likely to be retained (Odds
Ratio (OR)=3.404, p<0.001).
 Gender, Pell Grant recipient, and race/ethnicity do not have a significant effect on
retention.
 This model accounts for 26% of the variability in retention.
Is there a statistically significant relationship between
GPA and first-time, full-time freshman retention?
 Spring cumulative GPA is positively associated with retention.
 Students with a higher GPA are 77% more likely to be retained.
 The model is statistically reliable and explains 26% of the variation in first-time, full-
time freshman retention.
 Results are statistically significant and practically significant.
 This research is consistent with previous research that identifies GPA as one of
the most important predictors for retention and student outcomes.
 Academic strategies that improve first-year GPA could improve retention, future
academic success (GPA), and graduation.
 Library instruction can be used as a risk factor and as an intervention.
 Risk factor: freshmen who do not attend library instruction are at higher risk of
not being retained and having lower GPAs.
 Intervention: attending library instruction improves both student success and
student engagement.
 Library Instruction for a specific class
 Research appointments
 Workshops and library instruction not associated with a specific class
 Engagement is one of the few predictors of student success that universities can
influenced.
 Increased by providing meaningful educational activities that encourage cognitive
development.
 Interventions work by increasing student engagement which leads to improved
learning and retention.
 Behavioral engagement: participating in library instruction.
 Emotional engagement: how students feel during and after the instruction
session.
 Cognitive engagement: willingness and ability to participate in library instruction.
 Limitations:
 Scope & generalizability.
 Encourage other libraries to collect similar data and repeat methodology!
 At Odum Library
 Continue data collection and expand future research with a larger data-set.
 Examine all college years.
 Include other forms of library instruction and assistance.
 Supports student success
 Contributes to higher retention rates both directly and indirectly
 Strengthens all three aspects of engagement: emotional, cognitive, and behavioral
 Teaches students college success skills, research skills, and information literacy
skills
 Improves students’ confidence, comfort level in seeking help, and research abilities
 Benefits extend to college career and post-graduation
 Compelling narrative:
 Library instruction is an effective strategy for improving student engagement,
retention, and academic achievement.
 Gammell, W. J., Allen, G. J., & Banach, P. S. (2012). Leveraging existing data:
Indicators of engagement as early predictors of student retention. In Proceedings
of the First Annual North East Regional Learning Analytics (NERLA)
symposium (pp. 9-17).
 Matthews, J. R. (2012). Assessing library contributions to university outcomes:
The need for individual student level data. Library Management, 33(6/7), 389-
402.
 Murray, A., Ireland, A., & Hackathorn, J. (2016). The value of academic libraries:
Library services as a predictor of student retention. College & Research
Libraries, 77(5), 631-642
 Soria, K. M., Fransen, J., & Nackerud, S. (2013). Library use and undergraduate
student outcomes: New evidence for students' retention and academic
success. portal: Libraries and the Academy, 13(2), 147-164.
 Soria, K. M., Fransen, J., & Nackerud, S. (2014). Stacks, serials, search engines,
and students' success: First-year undergraduate students' library use, academic
achievement, and retention. The Journal of Academic Librarianship, 40(1), 84-91.
 Whalen, D., Saunders, K., & Shelley, M. (2009). Leveraging what we know to
enhance short-term and long-term retention of university students. Journal of
College Student Retention: Research, Theory & Practice, 11(3), 407-430.
 Wong, S. H. R., & Cmor, D. (2011). Measuring association between library
instruction and graduation GPA. College and Research Libraries, 72(5), 464-473.

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Data and Assessment in Academic Libraries: Linking Freshmen Student Success and Library Instruction

  • 2.  Library Instruction  Builds a foundation of research skills  Builds information literacy skills  Improves confidence and research ability
  • 3.  How do you convince skeptics that library instruction is valuable?  How do you market to professors who do not include library instruction in their courses?  Speak their language!
  • 4.  Colleges and universities are exploring ways to improve student outcomes, including student success and retention (Matthews, 2012; Murray, Ireland, & Hackathorn, 2016).  Colleges and universities are looking for data-based methods to:  Identify at-risk students  Improve student outcomes: grade point average (GPA), retention (Whalen, Saunders, & Shelley, 2009)  Library instruction could serve as a risk factor. Risk factors indicate when a student is at higher risk of leaving or having a lower GPA.  Library instruction could serve as a intervention. Interventions are designed to help at-risk students succeed.
  • 5.  Can we do a statistical analysis that shows library instruction supports GPA and retention?  Several researchers have investigated the relationship between library instruction and retention or GPA in recent years.  Attending a required, but not strictly enforced, library instruction session has been used as a predictor for retention (Gammell, Allen, & Banach, 2012).  Wong & Cmor found that the frequency of attending library instruction is positively associated with student achievement (2011).  Research on the relationship of library instruction and workshops on retention and GPA is not consistent (Soria, Fransen, & Nackerud, 2013; Soria, Fransen, & Nackerud, 2014).
  • 6.  Building on our existing culture  Existing statistics  Number of attendees at library instruction  Existing Assessment  Student Opinion of Library Instruction  Minute papers  Faculty Survey after Library Instruction  Library statistics  Door count  Number of books circulated  Interlibrary Loan and GIL Express  Electronic Resources  Technology statistics  Computer usage  Printer usage
  • 7.  Administration  Librarians  Data Warehouse  Professors
  • 8.  Why did we decide to collect LI attendance at in-person classes?  Simple, easy to implement.  Need LI attendance data if want to examine relationship between library instruction and GPA or retention.  Meeting with Data Warehouse  Help plan how to collect LI attendance data.  They collect lots of other data as a matter of course (GPA, retention, etc.).  How does data collection affect librarians teaching LI?  Added a step to teaching LI.  How does data collection affect professors?  Minimal impact – most already collect attendance.
  • 9.  Workflow  Schedule LI  Prepare print rosters from data warehouse widget  Collect attendance  Enter attendance in widget  File rosters  Challenges & Lessons Learned  Lost data  Checking accuracy  Repeat LIs – you get what you ask for
  • 10.  My dissertation examines the following relationships at Odum Library at Valdosta State University (VSU):  The relationship between library instruction and first-time, full-time freshmen retention.  The relationship between library instruction and first-year cumulative grade point average (GPA).  The relationship between GPA and first-time, full-time freshmen retention.
  • 11.  Institutional Data is collected from VSU’s Data Warehouse  Fall 2015 through Spring 2018  Independent Variables  Attending library instruction  GPA  Dependent Variables  GPA  First-time, full-time freshmen retention  Control Variables  Gender  Race/ethnicity  Pell Grant status (socioeconomic status)
  • 12.  Library instruction is positively associated with retention.  Chi-squared Test and binomial logistic regression.  Of the students who were retained, 72.60% did attend library instruction and 64.80% did not attend library instruction (p < 0.001).  If a student attended library instruction, they are 59% more likely to be retained (Odds ratio (OR) =1.419, p<0.001) than students who did not attend library instruction.  The model only accounts for 2.6% of the variation in retention. Is there a statistically significant relationship between attending library instruction and first-time, full-time freshman retention?
  • 13.  Library instruction is positively associated with GPA.  Multiple linear regression.  Overall R-square of 0.112, indicating that 11.2% of the variation in GPA is explained by the model.  Gender accounts for 4.2% of the variability observed in GPA  Pell Grant status accounts for an additional 2.5%  Library instruction attendance accounts for an additional 0.6%  Race/ethnicity accounts for an additional 3.9%  Library instruction attendance accounts for only 0.6% of the variation in the model. Is there a statistically significant relationship between attending library instruction and GPA?
  • 14.  Library instruction has a very small positive association with retention.  Students who attend library instruction are 59% more likely to be retained.  Model accounts for 2.6% of the variation in retention.  Library instruction has a very small positive association with GPA.  Attending library instruction accounts for 0.6% of the variation observed in GPA.  Size of the two relationships is consistent with previous research that found positive associations.  Cannot easily generalize or compare institutions because library instruction varies in its format and delivery.
  • 15.  GPA is positively associated with retention.  Binomial logistic regression.  If a student has a higher GPA, they are 77% more likely to be retained (Odds Ratio (OR)=3.404, p<0.001).  Gender, Pell Grant recipient, and race/ethnicity do not have a significant effect on retention.  This model accounts for 26% of the variability in retention. Is there a statistically significant relationship between GPA and first-time, full-time freshman retention?
  • 16.  Spring cumulative GPA is positively associated with retention.  Students with a higher GPA are 77% more likely to be retained.  The model is statistically reliable and explains 26% of the variation in first-time, full- time freshman retention.  Results are statistically significant and practically significant.  This research is consistent with previous research that identifies GPA as one of the most important predictors for retention and student outcomes.  Academic strategies that improve first-year GPA could improve retention, future academic success (GPA), and graduation.
  • 17.  Library instruction can be used as a risk factor and as an intervention.  Risk factor: freshmen who do not attend library instruction are at higher risk of not being retained and having lower GPAs.  Intervention: attending library instruction improves both student success and student engagement.  Library Instruction for a specific class  Research appointments  Workshops and library instruction not associated with a specific class
  • 18.  Engagement is one of the few predictors of student success that universities can influenced.  Increased by providing meaningful educational activities that encourage cognitive development.  Interventions work by increasing student engagement which leads to improved learning and retention.  Behavioral engagement: participating in library instruction.  Emotional engagement: how students feel during and after the instruction session.  Cognitive engagement: willingness and ability to participate in library instruction.
  • 19.  Limitations:  Scope & generalizability.  Encourage other libraries to collect similar data and repeat methodology!  At Odum Library  Continue data collection and expand future research with a larger data-set.  Examine all college years.  Include other forms of library instruction and assistance.
  • 20.  Supports student success  Contributes to higher retention rates both directly and indirectly  Strengthens all three aspects of engagement: emotional, cognitive, and behavioral  Teaches students college success skills, research skills, and information literacy skills  Improves students’ confidence, comfort level in seeking help, and research abilities  Benefits extend to college career and post-graduation
  • 21.  Compelling narrative:  Library instruction is an effective strategy for improving student engagement, retention, and academic achievement.
  • 22.
  • 23.  Gammell, W. J., Allen, G. J., & Banach, P. S. (2012). Leveraging existing data: Indicators of engagement as early predictors of student retention. In Proceedings of the First Annual North East Regional Learning Analytics (NERLA) symposium (pp. 9-17).  Matthews, J. R. (2012). Assessing library contributions to university outcomes: The need for individual student level data. Library Management, 33(6/7), 389- 402.  Murray, A., Ireland, A., & Hackathorn, J. (2016). The value of academic libraries: Library services as a predictor of student retention. College & Research Libraries, 77(5), 631-642  Soria, K. M., Fransen, J., & Nackerud, S. (2013). Library use and undergraduate student outcomes: New evidence for students' retention and academic success. portal: Libraries and the Academy, 13(2), 147-164.  Soria, K. M., Fransen, J., & Nackerud, S. (2014). Stacks, serials, search engines, and students' success: First-year undergraduate students' library use, academic achievement, and retention. The Journal of Academic Librarianship, 40(1), 84-91.  Whalen, D., Saunders, K., & Shelley, M. (2009). Leveraging what we know to enhance short-term and long-term retention of university students. Journal of College Student Retention: Research, Theory & Practice, 11(3), 407-430.  Wong, S. H. R., & Cmor, D. (2011). Measuring association between library instruction and graduation GPA. College and Research Libraries, 72(5), 464-473.