The document discusses the role of learning analytics in transforming library agendas, focusing on data collection and analysis to improve student learning experiences and outcomes. It highlights various benefits of learning analytics, including personalized learning, retention, and improved teaching methods, while addressing the needs for implementation in libraries through leadership and innovation. Additionally, it emphasizes the importance of understanding student engagement and the utilization of data to enhance educational practices.

1. Using analytics to transform
the library agenda
Dr Linda Corrin
@lindacorrin

2. DEFINITION
the measurement, collection, analysis and reporting of data
about learners and their contexts, for purposes of understanding
and optimizing learning and the environments in which it occurs
Society for Learning Analytics Research

3. Long P. & Siemens G. (2011) Penetrating the fog: analytics in
learning and education. EDUCAUSE Review 46, 31–40. Available at:
http://www.educause.edu/ero/article/penetrating-fog-analytics-
learning-and-education
Micro
Meso
Macro
Buckingham Shum, S., Knight, S., & Littleton, K. (2012).
Learning analytics. In UNESCO Institute for Information
Technologies in Education. Policy Brief.

4. Possibilities
Learning analytics
 Personalised learning
 Understanding the learning
process
 Information about the students’
context
 Pedagogical and assessment
improvements
 Understanding student
motivation and attitude
Academic analytics
 IT service provision
 Curriculum mapping
 Review of teaching structures
 Student support services
 Student retention
Drachsler, H., & Greller, W. (2012). The pulse of learning analytics. Understandings and expectations from the stakeholders.
In S. Buckingham Shum, D. Gasevic, & R. Ferguson (Eds.), 2nd International Conference Learning Analytics & Knowledge (pp.
120-129). April, 29-May, 02, 2012, Vancouver, BC, Canada.

5. Libraries and Student Success
Positive impact on grades a
Positive impact on retention b
Positive impact on grades and
retention c
a. 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.
b. Haddow, G. (2013). Academic library use and student retention: A quantitative analysis. Library & Information Science Research, 35(2), 127-136.
c. 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.

7. LA Implementation in Australia
1. Conceptualisation
2. Capacity & culture
3. Leadership
4. Rapid innovation cycle
5. Ethics

8. What do libraries
want/need to know?
How can learning analytics help answer these questions?
QUESTION:

9. Image source: https://edtechdigest.wordpress.com/2012/05/10/learning-analytics-the-future-is-now/

10. 1. Performance
2. Effort
3. Prior academic history
4. Student characteristics

11. Current Research
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12. Current Research
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13. Focus Groups
Student engagement
The learning experience
Quality of teaching and curriculum
Administrative functions associated with L&T
Student performance
‘At risk’ students
Attendance
Access to learning resources
Participation in communication
Performance in assessment
Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers.
In H. Carter, M. Gosper & J. Hedberg (Eds.), Electric Dreams. Proceedings ascilite 2013 Sydney. (pp. 201-205).

14. Focus Groups
Student engagement
The learning experience
Quality of teaching and curriculum
Administrative functions associated with L&T
Student performance +
= ? (ideal student)
Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers.
In H. Carter, M. Gosper & J. Hedberg (Eds.), Electric Dreams. Proceedings ascilite 2013 Sydney. (pp. 201-205).

15. Focus Groups
Student engagement
The learning experience
Quality of teaching and curriculum
Administrative functions associated with L&T
Student performance
Feedback
Corrin, L., Kennedy, G., & Mulder, R. (2013). Enhancing learning analytics by understanding the needs of teachers.
In H. Carter, M. Gosper & J. Hedberg (Eds.), Electric Dreams. Proceedings ascilite 2013 Sydney. (pp. 201-205).

16. Focus Groups
Student engagement
The learning experience
Quality of teaching and curriculum
Administrative functions associated with
learning & teaching
Student performance
 Greater understanding of how students develop knowledge
 Track prior knowledge and it’s development through learning activities
 Data?

17. Focus Groups
Student engagement
The learning experience
Quality of teaching and curriculum
Administrative functions associated with
learning & teaching
Student performance
 Automated textual analysis of messages sent to student support services
 Assessment (formative and summative) to identify areas for review
 Access to support resources

18. Focus Groups
Student engagement
The learning experience
Quality of teaching and curriculum
Administrative functions associated with
learning & teaching
Student performance
 Assessment of consistency of student placements
 Enrolment and profiling of tutorial groups
 Tracking safety requirements for field trips
 Guidance for students on future subject selection

19. Interviews
Interviews with 12 teaching academics (UoM, Macquarie, UniSA)
1. Fairly basic analytics requests
2. Focus on engagement analytics
3. Limited use of technological tools (blended)
4. Concerns over ability to interpret data
Kennedy, G., Corrin, L., Lockyer, L., Dawson, S., Williams, D., Mulder, R., Khamis, S., & Copeland, S. (2014). Completing
the loop: returning learning analytics to teachers. In B. Hegarty, J. McDonald, & S.-K. Loke (Eds.), Rhetoric and Reality:
Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 436-440).

20. Loop

21. Loop

24. Loop

27. Learning Design
“Learning design
provides a semantic
structure for analytics”
Mor, Ferguson & Wasson, 2015
“a documentation of
pedagogical intent”
Lockyer, Heathcote & Dawson, 2013

29. Interaction with resources
Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning.
Metacognition and Learning, 2(2-3), 107-124.

30. GIVING THE DATA TO STUDENTS…

31. Student Perspectives
“I just log into the [LMS] to download learning materials
and print them. I do not think my online learning
behaviours such as log-ins would reflect my general
efforts for learning and learning outcomes”
Park, Y., & Jo, I. H. (2015). Development of the Learning Analytics Dashboard to Support Students' Learning Performance. Journal of Universal
Computer Science, 21(1), 110-133.
 Plan learning schedule
 Manage learning processes
 Set learning goals
 Get an objective and
accurate perspective
 Do not want such data
to impact final score
and grade

32. Student Dashboards
Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In B. Hegarty, J.
McDonald, & S.-K. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 629-633).

33. JISC Student Learning Analytics App
Source: Sclater, N. (2015) What do students want from a learning analytics app?.
http://analytics.jiscinvolve.org/wp/2015/04/29/what-do-students-want-from-a-learning-analytics-app/

34. Situation Theory Question Data Representation Timing
Situation Theory Question Data Representation Timing
Planning for Libraries

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© Melbourne Centre for the Study of Higher Education, The University of Melbourne
2016