1.
Where is Theory
in Learning Analytics?
Srecko Joksimovic, University of South Australia
@s_joksimovic

2.
Overview
• History & Drivers
• Data
• Theory
• Overview of Relevant Studies
• SRL
• Engagement
• Social Interactions

3.
Context
Exploring human and artificial
cognition to understand knowledge
processes and their impact on
society
Centre for Change and Complexity in Learning

4.
History & Drivers

5.
The First LAK Conference
A Brief History
How everything started…
2011
The First Handbook of LA2017

6.
Pursuit for
Personalized
and Adaptive
Learning

7.
How can we extract
value from these
big sets of
data?

8.
Education is no different
Substantial investments in analytics
Ease access to learner data
Increased adoption of personal technologies

9.
Data

10.
Data We Collect

11.
No semantic or causal analysis is required
Correlation is enough?
Big data's impact on
digital marketing landscape
The Power of Data
Data
“All models are wrong,
but some are useful.”
- George E. P. Box
It’s not just quantity – quality matters too

12.
“What counts as a meaningful finding
when the number of data points is
so large that something will always be
significant?”
Data in Education
Validity
Generalizability
Importance of feedback
Privacy, Ethics
What is missing?
Wise and Shaffer (2015), pp.6

13.
Theory

14.
01
02
03
What do learning sciences have to do
with learning analytics?
Paul Kirschner
Available at https://goo.gl/5Uzf12
LAK'16 keynote
- Just about everything!

15.
our learning analytics
are our pedagogy
- Buckingham Shum (2012)
- Knight et al (2014)
Epistemology
PedagogyAssessment

16.
Designing for Learning
(Goodyear and Carvalho, 2014)

17.
01
02
… with larger amounts of data, theory
plays an ever-more critical role in
analysis
Theory matters…
Wise and Shaffer (2015)

18.
Adoption of theory
Consolidated Model
Contribute to theory
Design
Theory
Data Science
Collection
Measurement
Analysis
Reporting
Interaction & Visualization
Learning
Study

19.
Self-Regulated Learning

20.
Four-stage Model of SRL
(Winne and Hadwin)
A strong metacognitive
perspective
A Socio-cognitive Perspective of SRL
Grounded by Three Models
(Zimmerman)
SRL model is organized in three
phases: forethought, performance
and self-reflection
The Role of Motivation
In SRL
(Pintrich)
The integration of motivational
constructs in SRL
Model of Adaptable Learning
(Boekaerts)
SRL is assumed to necessitate
interaction between
diverse (e.g. metacognitive,
motivational and emotional)
control systems
Socially Shared Regulation
(Järvelä and Hadwin)
Collaboration poses cognitive,
motivational, social, and
environmental challenges
Models of SRL (Panadero, 2017)

21.
Winne and
Hadwin’s
(1998)
model of SRL

22.
Learning Analytics
Dashboards
Systematic Review
- are rarely grounded in learning theory;
- cannot be suggested to support metacognition;
- do not offer any information about effective
learning tactics and strategies; and
- have significant limitations in how their
evaluation is conducted and reported
(Matcha, et al., 2019)

23.
Technology Use
Profiles
- task-focused users,
- content-focused no users,
- no users,
- highly intensive users,
- content-focused intensive users, and
- socially-focused intensive users
(Kovanović, et al., 2015)

24.
Engagement

25.
Reschly and Christenson (2012, p.10)

26.
(Joksimović, et al., 2019)

27.
Operationalization of
Engagement
- Cognitive engagement,
- Affective engagement,
- Behavioural + Academic engagement
(Fincham, et al., 2019)

28.
Social Interactions

29.
Social Network Analysis
 Actions are viewed as interdependent
 Ties as channels for flow of resources
 Structural environment as opportunity or constraint
 Structure (e.g., social, economic, political) as lasting patterns
of relations among actors

30.
Russo and Koesten (2005)
prestige (in-degree) Cognitive learning
outcomecentrality (out-degree)
degree centrality
Course
grade
Cho et al. (2007)
closeness centrality
betweenness centrality
Jiang et al. (2014)
degree centrality
GPAcloseness centrality
betweenness centrality
eccentrality
Gašević et al. (2013)
degree centrality
Course
grade
closeness centrality
betweenness centrality
degree centrality
Course
grade
closeness centrality
betweenness centrality
Positive, statistically
significant association
Note:
No statistically
significant association
Social Centrality and
Learning Outcome
Does higher social centrality imply more benefit?
NOT NECESSARILY… (Krachardt, 1999)

31.
Importance of
Social Dynamics
• Structural
properties: degree,
betweenness,
closeness centrality
Network
• Reciprocity
• Strength of ties
• Homophily
Social
dynamics
• Final course grade
• Learning gain
Learning
outcome

32.
Designing a Course with Learning
Analytics in Mind
Summary

33.
Where is Theory
in Learning Analytics?
Srecko Joksimovic, University of South Australia
@s_joksimovic

34.
References
Joksimović S., Kovanović V., & Dawson S.: “A Decade Later: The Journey of Learning Analytics”, HERDSA Review of Higher Education, 2019 Vol 6, pp 37-63;
Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired magazine, 16(7), 16-07.
Wise, A. F., & Shaffer, D. W. (2015). Why Theory Matters More than Ever in the Age of Big Data. Journal of Learning Analytics, 2(2), 5-13.
https://doi.org/10.18608/jla.2015.22.2
Knight, S.; Buckingham Shum, S., & Littleton, K. (2013). Epistemology, pedagogy, assessment and learning analytics. In: Third Conference on Learning Analytics and
Knowledge (LAK 2013), 8-12 Apr 2013, Leuven, Belgium, pp. 75–84.
Gašević, D., Kovanović, V., & Joksimović, S. (2017) Piecing the learning analytics puzzle: a consolidated model of a field of research and practice, Learning: Research and
Practice, 3:1, 63-78, DOI: 10.1080/23735082.2017.1286142
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in psychology, 8, 422.
Greene, J. A., & Azevedo, R. (2007). A theoretical review of Winne and Hadwin’s model of self-regulated learning: New perspectives and directions. Review of educational
research, 77(3), 334-372.
Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. Motivation and self-regulated learning: Theory, research, and applications, 2, 297-
314.
Matcha, W., Ahmad Uzir, N., Gasevic and A. Pardo, "A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective," in
IEEE Transactions on Learning Technologies. doi: 10.1109/TLT.2019.2916802
Kovanović, V, Gašević, D, Joksimović, S, Hatala, M & Adesope, O 2015, 'Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in
asynchronous online discussions’, Internet and Higher Education, vol. 27, pp. 74–89. https://doi.org/10.1016/j.iheduc.2015.06.002
Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In Handbook of research on
student engagement (pp. 3-19). Springer, Boston, MA.
Joksimović, S., Poquet, O., Kovanović, V., Dowell, N., Mills, C., Gašević, D., ... & Brooks, C. (2018). How do we model learning at scale? A systematic review of research on
MOOCs. Review of Educational Research, 88(1), 43-86.
Fincham, E., Whitelock-Wainwright, A., Kovanović, V., Joksimović,S., van Staalduinen, JP, and Gašević, D. 2019. Counting Clicks is Not Enough: Validating a Theorized Model
of Engagement in Learning Analytics. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK19). ACM, New York, NY, USA, 501-510.
DOI: https://doi-org.access.library.unisa.edu.au/10.1145/3303772.3303775
Joksimović, S., Manataki, A., Gašević, D., Dawson, S., Kovanović, V., and de Kereki, I.F. 2016. Translating network position into performance: importance of centrality in
different network configurations. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK '16). ACM, New York, NY, USA, 314-323.
DOI: https://doi.org/10.1145/2883851.2883928