Presented at WiLSWorld 2016 on August 2nd, 2016 by Kimberly Arnold, Evaluation Consultant, UW-Madison Learning analytics has recently burst onto the educational scene. A process that helps optimize the teaching and learning environment for students, educators, and administrators, learning analytics has proven it is more than a passing trend. This session will offer a primer on learning analytics and share examples of how libraries are getting involved.

1. Learning Analytics and
Libraries: A Natural
Partnership
WiLS World 2016
August 2, 2016
Kimberly Arnold

2. Agenda
• What is Learning Analytics
• Learning Analytics and Libraries
• Accountability
• Student Success
• Public Libraries
• Other Intersections
• Discussion

3.  Who b u
 data

4. What is this learning
analytics thing?

6. Source: Barua, A., Mani, D., & Mukherjee, R. (2012). Measuring the business
impacts of effective data. Report accessed at http://www. sybase.
com/files/White_Papers on Sep, 15, 2015.

7. Educause’s Top 10 IT Issues 2016
1. Information security
2. Optimizing educational technology
3. Student success technologies
4. IT workforces hiring and retention
5. Institutional data management
6. IT funding models
7. BI & Analytics
8. Enterprise application integrations
9. IT organizational development
10. Elearning and online education
Gartner Top Business Trends in HE 2016
1. Student success
2. Competency based education
3. Reinventing credits
4. Analytics everywhere
5. Institutional branding
6. Breaking boundaries
7. Rethinking business models
8. Increased political interventions
9. Innovative learning spaces
10. E-research
2016 ALA Top Technology Trend
Learning Analytics

8. 8/5/2016University of Wisconsin–Madison 8
Learning analytics is the measurement, collection,
analysis, and reporting of data about learners and their
contexts, for the purposes of understanding and
optimizing learning and the environments in which it
occurs.
~Society for Learning Analytics Research

9. Adapted from: Eckerson, W. W. (2006) Performance Dashboards: Measuring,
Monitoring, and Managing Your Business. John Wiley & Sons, Inc. Hoboken, NJ.
R
E
F
I
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Developing “Actionable Intelligence”

10. Learning Analytics and Libraries:
Accountability

11. Learning Analytics & Metrics
Project
 View library resources usage by subject,
course, and level of student attainment
 Reveal statistically significant trends
 View resource usage against NSS
 What “types” of learners are using which
resources least/most
http://jisclamp.mimas.ac.uk/

12. Other Library Examples
 There is a correlation between student
(grade) attainment and library activity data–
Stone & Ramsden (2012); Stemmer & Mahan
(2015)
 There is a link between library usage and
retention—Haddow & Joseph (2010); Soria,
Fransen, & Nackerud (2013)
 There is a link between GPA and library
workshop attendance –Wong & Cmor (2010)
 Being part of library-related activities
correlated with collaborative learning and
student engagement—Laird & Kuh (2005)

13. Learning Analytics and Libraries:
Student Success

14. Predictive analytics encompasses a variety of
statistical techniques from predictive modeling that
analyze current and historic facts to make predictions
about future or otherwise unknown events.
~The Data Warehouse Institute

16. …an example

17. Challenge: How do you find the student at risk?

18. Challenge: How do you find the student at risk?

19. Student Dashboard
 Dashboard measure students’ engagement—
this is a stronger predictor of success than
back ground characteristics
 Library usage (composite) is more predictive
than VLE
 When interventions are presented, library
usage goes up
 Transparency is critical
https://analytics.jiscinvolve.org/wp/files/2016/04/CASE-STUDY-I-
Nottingham-Trent-University.pdf

20. JISC Student Activity Pulse

21. JISC Reports

22. Use of Digital Resources
 Lessen focus on “at risk”
 What are successful pattern in terms of
digital learning resources access
 Self-regulation lens and course performance
(more empirical)
 the use of learning resources influences
course performance differently for different
groups of students
https://www.researchgate.net/publication/293333321_Student_diffe
rences_in_regulation_strategies_and_their_use_of_learning_resource
s_implications_for_educational_design

23. Learning Analytics and Libraries:
Public Libraries

24. Analytics on Demand
Combines LIS data with US demographics,
census, voter registration, and geographical
data
 Emmet O’Neal Library: Data informed
programming and collection development
 King County Library System: Targeted
outreach
 Sacramento Public Library: Patron profiles
e-book and studio books profiles

25. Learning Analytics and Libraries:
Other Areas

26. Other Areas of Interest
 Open Educational Resources
 Space redesign/collaborative learning
 Course Design/Content Selection
 E-books (and general e-resources)
 Data/Information Literacies
 Privacy/Ethics

27. Recent Library Resources
College & Research Libraries May 2016; 77 (3)
http://crl.acrl.org/content/current
UC Berkeley's Academic Innovation Studio’s three part webinar
series: Learning Analytics and the Academic Library
http://ais.berkeley.edu/events/learning-analytics-and-
academic-library-state-art-and-art-connecting-library-campus
Advancing Learning Transforming Scholarship
http://www.ala.org/acrl/learninganalytics

28. Upcoming Events
Library Assessment Conference
Oct 31-Nov 2
Arlington, VA
http://libraryassessment.org/schedule/

29. References
 Bos, N., & Brand-Gruwel, S. (2016, April). Student differences in regulation strategies
and their use of learning resources: implications for educational design. In Proceedings
of the Sixth International Conference on Learning Analytics & Knowledge (pp. 344-353).
ACM.
 De Jager, K. (1997). Library Use and Academic Achievement. South African Journal of
Library & Information Science, 13(2), 26-30.
 Haddow, G., & Joseph, J. (2010). Loans, logins, and lasting the course: Academic library
use and student retention. Australian Academic & Research Libraries, 41(4), 233-244.
 Jantti, M., & Cox, B. (2013). Measuring the value of library resources and student
academic performance through relational datasets. Evidence Based Library and
Information Practice, 8(2), 163-171.
 Laird, T. F. N., & Kuh, G. D. (2005). Student experiences with information technology
and their relationship to other aspects of student engagement.Research in Higher
Education, 46(2), 211-233.
 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.
 Stemmer, J. K., & Mahan, D. M. (2015). Investigating the Relationship of Library Usage
to Student Outcomes. College & Research Libraries, crl15-704.
 Stone, G., & Ramsden, B. (2012). Library Impact Data Project: looking for the link
between library usage and student attainment. College & Research Libraries, crl12-406.
 Wong, S. H. R., & Cmor, D. (2010). Measuring association between library instruction and
graduation GPA. College & Research Libraries, crl-151.

30. kimberly.arnold@wisc.edu