1. The document discusses the use of learning analytics to understand student learning and optimize teaching practices. It describes how analytics can provide insights into student performance, engagement, and retention at various levels from the individual to the institution. 2. Interviews with teachers found they are interested in analytics about student engagement and performance but have concerns about interpreting data. Teachers want analytics to help understand ideal students and provide feedback to improve teaching. 3. A conceptual framework is presented that links learning analytics to learning design to provide context for analyzing educational activities and interactions with resources. Planning questions are also outlined to help educators implement learning analytics.

1. Video Delivery Partner

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

3. 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

4. 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.

5. 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.

6. Hot off the press - JISC Report
 As a tool for quality assurance and
quality improvement
 As a tool for boosting retention rates
 As a tool for assessing and acting upon
differential outcomes among the
student population
 As an enabler for the development
and introduction of adaptive learning

7. 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.

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

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

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

13. Teachers & Students
0101010101010101010101010
?

14. Teachers & Students
0101010101010101010101010
?010101010101010101101010
0101010101010101010101010
0101010101010101010101010

15. Teachers & Students
0101010101010101010101010
?010101010101010101101010
0101010101010101010101010
0101010101010101010101010
FEEDBACK

16. Teachers
1. Focus groups
9 focus groups
University of Melbourne
2. Interviews
12 individual interviews
3 Australian universities

17. Focus Groups
Student performance
Student engagement ‘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).

18. Focus Groups
Student performance
Student engagement
+
= ? (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).

19. Focus Groups
Student performance
The learning experience
Quality of teaching and curriculum
Administrative functions associated with L&T
Student engagement
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).

20. 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).

21. Loop

22. Loop

23. Loop

26. Loop

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

30. Learning Analytics for Learning Design
Conceptual Framework
Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gasevic, D., Mulder, R., Williams, D., Dawson, S., Lockyer, L. (2016). A
conceptual framework linking learning design with learning analytics. In Proceedings of the 6th International Conference
on Learning Analytics and Knowledge. New York: ACM.

31. Discussion Activities

33. 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.

34. GIVING THE DATA TO STUDENTS…

35. Students
How?
What?
When?
Privacy & Ethics

36. Vendor Solutions

37. 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

38. Student Dashboards

39. Nottingham Trent - Student Dashboard
Source: https://www.ntu.ac.uk/current_students/document_uploads/179129.pdf

40. 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/

41. JISC Student Learning Analytics App
Source: Sclater, N. (2015) Student app for learning analytics: functionality and wireframes.
http://analytics.jiscinvolve.org/wp/2015/08/21/student-app-for-learning-analytics-functionality-and-wireframes/

42. Situation
Theory/Design
Question
Data
Representation
Timing
Planning for Learning Analytics
What is the problem/issue you want to address?
What learning theory or design informs the situation?
What is the specific question(s) you want to answer about the situation?
What data do you need to answer the question?
How can this data be represented in a way it will be meaningful?
When would it be best to receive this information?

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

44. melbourne-cshe.unimelb.edu.au
© Melbourne Centre for the Study of Higher Education, The University of Melbourne
2016

45. Video Delivery Partner