Presentation on Learning Analytics at the Moodle MOOT in Greece 26 11 2021. Part of the TEASPILS project

1. Agreement nr: 2020-1-ES01-KA203-082258
Learning Analytics for
Learning
Using student and teacher data to support
learning effectively
Wolfgang Greller - Moodle Moot, Greece 26-27.Nov.2021

2. What‘s in this talk?
 Short overview of the field of Learning Analytics
 Why use Learning Analytics for SRL?
 Dealing with evidence in form of learner data
 What skills are needed to use Learning Analytics?

3. 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 educational 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)

4. 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)

5. 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 evidence, 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“
– evidence-based learning!

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

7. 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 settings, unobtrusive collection)
• Real-time information at micro and macro level

8. Why Learning Analytics?
All kinds of learning 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
This learner-centred view supports personalisation of learning

9. Analytics for evidence-based learning
We see Learning Analytics as a reflection amplifier and part of objective
learning evidence that triggers a learner’s and/or teacher’s 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

10. Exploitation of learner data for learning

11. Digital Competences for Educators
Data Literacy:
“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!

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

13. Potential shortfalls
• 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!

14. Transfering data to learning
Targeted intervention (guided by teacher):
3 areas of analytics-induced change in (self-)reaction:

15. 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.
For students: Reflect own learning performance, plan for improvements.
Manipulating weighting of indicators: what is more important to get a focussed
understanding.
Requires: scientific thinking!

16. Ethical and legal knowledge
“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)
Requires transparency!

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

18. 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