This document discusses using learning analytics to support self-regulated learning (SRL). It defines learning analytics as using data from educational activities to identify patterns and provide information to improve learning. Learning analytics can support SRL by providing timely feedback to help learners monitor progress, adjust strategies, and reflect on performance. However, effectively using learner data for SRL requires competencies like data literacy, critical thinking, and ensuring ethical and responsible use of student data.

1. Learning Analytics
for SRL
Cómo implementar las analíticas de
aprendizaje en el aprendizaje autorregulado
eMadrid conference 26-27 June 2019
Wolfgang Greller (PHW)

2. Wolfgang Greller Profile
 Scholar
 Project Manager
 University Manager
 EU Evaluator
 Flexible Networker
 Lifelong Learner

3. Networked Universities

4. Vienna University of
Education
 Vice-Principal for Research and Quality Assurance
 Professor for LT-Innovation and Language Education

5. What‘s in this talk?
• Short overview of the field of Learning Analytics
• Why use Learning Analytics for SRL practice?
• Dealing with evidence in form of learner data
• What skills are needed to use Learning Analytics for
SRL (and in general)?
• Development of an EQF-mapped framework of
practical competences

6. SLIDEshow project
• Erasmus+ KA2 Strategic Partnership to promote SRL
among Primary School Teachers and their pupils
• Extending preceding tMail project (SRL mobile app)
• Focus on (self-)assessment of SRL knowledge and
competences
• Runtime: Nov 2017-Oct 2020
• Five partner consortium: VUB, UO, PHW, Go!,
Doukas

7. What is Learning Analytics
Basically, it is the use of „Big Data“ in education for the
sake of improving learning.
Definition:
“the application of analytic techniques to analyse educa-
tional data, including data about learner and teacher
activities, to identify patterns of behaviour and provide
actionable information to improve learning and learning-
related activities”
(Van Harmelen & Workman, 2012)

8. Why Learning Analytics?
Students (and teachers) appreciate:
- Better understanding of (own) learning behaviours
- Personalised assistance
- Timely feedback and intervention
- Information that facilitates independent learning
(Wong, 2017; Tsai, Scheffel & Gasevic, 2018)

9. Distinguishing features of
Learning Analytics
- Scale: large digital datasets (big data) with micro-to-macro
level information across an entire learner population
- Speed: LA provides automated real-time information, on-
demand timely feedback for learner positioning
- Sources: can process data from a variety of sources, incl.
different sensors (e.g. heart rate monitor, face recognition,
GPS, etc.)
- Authenticity: collects behavioural data unobtrusively, not
influenced by „opinions“

10. Six dimensions of Learning
Analytics
(Greller & Drachsler, 2012)

11. What is learning evidence?
Objective information about learning process and
success (progress, achievements)
• Uses direct observation
• Uses scientific research from Learning Sciences
• Uses conclusive evidence (e.g. through causality)
• Uses learning data information
• Authentic information (real setting, unobtrusive)
• Real-time information at micro and macro level

12. Why Learning Analytics
for SRL?
SRL requires (regular) feedback during the learning
process
• To monitor progress and position
• To adjust self-regulation strategies
• To plan next steps
• To keep focus (goal orientation)
• To compare with others
• To identify and acquire successful learning strategies
• To motivate the learner

13. Learning Analytics for
evidence-based SRL
We see Learning Analytics as a reflection amplifier
and part of objective learning evidence that triggers
a learner and/or teacher response. This strongly links
it to the self-evaluation strategy of the Zimmerman
model of SRL (Zimmerman & Moylan 2009)
Forethought
phase
Performance
phase
Self-reflection
phase

14. Learning Analytics for
evidence-based SRL
Forethought
phase
Performance
phase
Self-reflection
phase
Performance phase:
Self-control
• Task strategies
• Self-instruction
• Time management
• Environmental structuring
Self-observation
• Metacognitive monitoring
• Self-recording
Self-reflection phase:
Self-judgement
• Self-evaluation
• Causal attribution
Self-reaction
• Self-satisfaction
• Adaptation
• Self-defense
(Zimmerman & Moylan 2009)

15. SRL practice:
the Quantified Self
The „Quantified Self“ movement promotes self-
observation and reacting to one‘s own performance
data.
Mostly used for (self-selected) behavioural changes: quit
smoking, lose weight, get fit, sleep well,…
Learners take full charge of their own progress and
success. Reflection, context, and instruments are key.
e.g. fitbits, GPS watches, heart rate monitor, step
counter, mobile apps, fitness studio tools

16. Exploitation of learner data
for SRL
Three self-regulatory processes in SRL (Zimmerman, 1989):
• Self-observation
• Self-judgement
• Self-reaction
Data from learner behaviours can support these processes
BUT: using learning analytics requires a number of
competences

17. Exploitation of learner data
for SRL

18. Exploitation of learner data
for SRL

19. Digital Competences for
Educators
“Furthermore, the use of digital technologies in education, whether for
assessment, learning, administrative or other purposes, results in a wide
range of data being available on each individual learner’s learning
behaviour. Analysing and interpreting this data and using it to help make
decisions is becoming more and more important – complemented by the
analysis of conventional evidence on learner behaviour.”
(DigCompEdu: Redecker, 2017)
Seeing data in context!
Data Literacy:

20. Self-observation skills
Self-observation, self-monitoring, self-recording are
indispensable parts of SRL (Zimmerman, 1989)
…leads to self-judgement
Requires hypothesis building and operationalisation!
instruments, units, collection process

21. Visualisations
Data visualisation interpretation and analysis can be
quite complex.
Requires interpretation and sensemaking!

22. Shortfall awareness
• Data and analytics are not neutral. Analytics are narratives. Data leads
to hypotheses (potential explanations) that need to be tested and
verified.
• Mostly come in probabilities not in clear answers.
• Pedagogic bias toward instructionism/behaviourism.
• Data not only shows learner information but implicitly also
teacher/context information.
• Cheating risks: gaming the system.
Requires critical thinking and holistic learner
perspective!

23. Transfering data to learning
SRL: Self-reaction based on self-judgement
or (guided by teacher): targeted intervention
3 areas of analytics-induced change in self-reaction:
materials)

24. Transfering data to learning
Mutual influences between learning evidence and
learning design.
Applying the right action after looking at the data evidence.
Reflect on learning design: what needs to change, how to
go about changing it? e.g. correcting misconceptions
Manipulating weighting of indicators: what is more
important to get a focussed understanding.
Requires: scientific thinking!

25. Ethical and legal knowledge
Requires transparency!
• Data protection (GDPR)
• Privacy protection: tracing of behaviours and activities
• Student surveillance vs. student support
• Obligation to act
• Data security
“Ethical and responsible data use is part of knowing how to
use data, and that knowledge focuses on how to protect
student privacy and maintain confidentiality of student data.”
(Mandinach, Parton, Gummer, & Anderson, 2015)

26. SRL data in a social context
Knowledge and skills are not solely individual
achievements, but are developed, carried forward,
and passed on through interaction and collaboration.
(Ferguson & Shum, 2012)
• Collaborative Learning: groups, roles, etc. (CSCL)
• Social positioning of the learner: performance
compared to peers (past & present), gamification
Requires data in social context!

28. Available at: https://bit.ly/2RsPku3

29. THANK YOU VERY MUCH !!!
Contact: wolfgang.greller@phwien.ac.at

30. References
• Ferguson, R., & Shum, S. B. (2012). Social learning analytics: five approaches. In Proceedings of the 2nd International Conference on
Learning Analytics and Knowledge - LAK ’12 (p. 23). Vancouver, British Columbia, Canada: ACM Press.
https://doi.org/10.1145/2330601.2330616Van Harmelen, M., & Workman, D. (2012). Analytics for learning and teaching. CETIS
Analytics Series, 1(3), 1–40
• Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of
Educational Technology & Society, 15(3), 42–57
• Mandinach, E. B., Parton, B. M., Gummer, E. S., & Anderson, R. (2015). Ethical and appropriate data use requires data literacy. Phi
Delta Kappan, 96(5), 25–28. https://doi.org/10.1177/0031721715569465
• Redecker, C. (2017, November 20). European Framework for the Digital Competence of Educators: DigCompEdu. Retrieved 10
August 2018, from https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/european-framework-
digital-competence-educators-digcompedu
• Tsai, Y.-S., Scheffel, M., & Gasevic, D. (2018). Enabling Systematic Adoption of LA through a Policy Framework. Presented at the
ECTEL 2018, Leeds
• Wong, B. T. M. (2017). Learning analytics in higher education: an analysis of case studies. Asian Association of Open Universities
Journal, 12(1), 21–40. https://doi.org/10.1108/AAOUJ-01-2017-0009
• Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329
• Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J.
Dunlosky & A. C. Graesser (Eds.), Handbook of Metacognition in Education (pp. 299-315). New York: Routledge