Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Â
Analytics Revolution! Using a Predictive Model to Measure the Libraries' Impact on Student Success
1. Analytics Revolution! Using a
Predictive Model to Measure
the Librariesâ Impact on
Student Success
Joleen McInnis, Health & Life Science Librarian
Lucinda Rush, Instruction Librarian
Leo S. Lo, Associate University Librarian for
Research & Learning
Virginia Library Association Conference
Williamsburg, VA September 27, 2018
4. 4
â⌠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
behaviors with attainment of other outcomes. Until
librarians do that, they will be blocked in many of
their efforts to demonstrate value.â
Megan Oakleaf (2010)
The Value of Academic Libraries
Analytics and Libraries
7. 7
Misuse of data
Image Credit: https://www.dailybulletin.com/2018/03/22/facebook-doesnt-want-you-to-worry-about-cambridge-analytica-political-cartoons/
12. 12
Our Study
ď§ Does participation in library instruction or an
individual research consultation impact:
ď§Course Grade?
ď§Overall GPA?
ď§Retention?
ď§Graduate Rate?
ď§Likelihood of using library services again?
13. 13
1. Getting Started
⢠Learning how
to use EAB
⢠IRB
⢠Seeking help
from partners:
Advising &
Tutoring, ITS,
IRB,
Assessment
2. Participant
Consent
⢠Faculty
permission
⢠Info Lit & Data
Lit materials
⢠Student
consent
3. Add Research
Consultations
⢠Learning how
to use EAB
(again)
⢠Training all
liaison
librarians
14. 14
4. Analyze
⢠Analysis will begin when
we have a full
academic year
⢠Initial findings may
provide valuable data
that could be used to
impact classroom
teaching
⢠Full analysis to begin in 5
years.
5. Evaluate
⢠Is there a correlation
between library
instruction and student
success indicators?
⢠What have we learned
that an be applied to
our programming and
teaching?
6. Repeat
⢠What did we learn?
⢠Are the Results
Consistent?
⢠How can we apply
what we learn to
improve student
learning outcomes and
positively influence
student achievement?
16. 16
IRB Application
â˘Initially applied as
âexemptâ
â˘Study is actually classified
non-exempt because of
how we will use data and
because of access to
FERPA information.
â˘Lengthy application
requiring methodologies,
rationale, informed
consent documentation,
etc.
IRB Review
â˘Feedback from evaluators
â˘Revisions based on
feedback
â˘Resubmission
â˘IRB liaison very helpful in
guiding us through the
rigorous process,
explaining requirements,
and giving a broader
perspective about rules
related to consenting
participants that were not
clear.
Amendments
â˘Informed Consent Process
refined
â˘Creation of Informed
consent video to
standardize presentation
of concepts and the study
â˘Personnel: Needed to
increase librarians able to
consent students
â˘Update CITI training
17. 17
ď§ Pass out forms
ď§ Show brief video
ď§ Collect all forms
Informed Consent & Privacy
21. 21
Future Actions
Add in Research
Consults
Include ALL
teaching
librarians
Identify funds
Data
Management &
Analysis Workflow
Incorporate new
SSC platform &
consider more
uses
Expand Data
Literacy
programming
ANDâŚ
⢠Can we improve the
informed consent
process?
⢠Are there other
possibilities for campus
partnerships?
22. 22
ď§ Association of College and Research Libraries. Value of Academic Libraries: A Comprehensive Research
Review and Report. Researched by Megan Oakleaf. Chicago: Association of College and Research
Libraries, 2010.
ď§ Butler, Kathy and Jason Byrd. âResearch Consultation Assessment: Perceptions of Students and Librarians.â The
Journal of Academic Librarianship, vol. 42, no. 1 , January 2016, pp. 83-86. ScienceDirect,
http://dx.doi.org/10.1016/j.acalib.2015.10.011. Accessed 29 June 2017.
ď§ Haddow, Gaby. âAcademic Library Use and Student Retention: A Quantitative Analysis.â Library & Information
Research, vol. 35, no. 2, April 2013, pp.127-136. http://dx.doi.org/10.1016/j.lisr.2012.12.002. Accessed 7
July 2017.
ď§ Matthews, Joseph R. âAssessing Library Contributions to University Outcomes: The Need for Individual Student
Level Data.â Library Management, vol. 33, no. 6/7, Spring 2012, pp. 389-402. EmeraldInsight,
https://doi.org/10.1108/0143512121166203. Accessed 31 October 2017.
ď§ Mezick, Elizabeth M. âRelationship of Library Assessment to Student Retention.â The Journal of Academic
Librarianship, vol. 41, no. 1, January 2015, pp. 31-36. ScienceDirect,
http://doi.org/10.1016.j.acalib.2014.10.011. Accessed 15 January 2018.
ď§ Soria, Krista M., et al. âLibrary Use and Undergraduate Student Outcomes: New Evidence for Studentsâ
Retention and Academic Success.â portal: Libraries and the Academy, vol. 13, no. 2, April 2013, pp.147-
164. Project Muse, https://doi.org/10.10353/pla.2013.0010. Accessed 31 October 2017.
ď§ Thorpe, Angie, et al. âThe Impact of the Academic Library on Student Success: Connecting the Dots. portal:
Libraries and the Academy, vol. 16, no. 2, April 2016. pp. 373-392. Project Muse,
http://muse.jhu.edu/article/613847. Accessed 29 June 2017.
Selected References
23. 23
Comments or Questions?
THANK YOU!
Contacts:
Joleen McInnis, jwesterd@odu.edu
Lucinda Rush, lrush@odu.edu
Leo S. Lo, llo@odu.edu