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.
8.
9. LA Implementation in Australia
1. Conceptualisation
2. Capacity & culture
3. Leadership
4. Rapid innovation cycle
5. Ethics
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).
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.
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.
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
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