Learning analytics as an academic research space has been growing in influence for nearly a decade. Campuses globally are deploying learning analytics to address a range of challenges including student dropout, poor engagement and targeted marketing as well as predict teaching and resource needs. As a field, learning analytics has advanced rapidly both as a research domain and as a practical on-campus activity to increase organizational use of data. In this presentation, Dr. George Siemens will explore both the research and the practice of analytics in education, focusing on the development of the Society for Learning Analytics, models for research and organizational data use and growing sophistication of data collection through psychophysiological approaches.

1. Learning
Analytics:
its emergence,
trends, and systemic
George Siemens
University of Michigan
January 23, 2017

2. Emergence
❖ A quick background:
❖ LAK11
❖ LASI
❖ JLA
❖ Organizational structure
❖ Also, it’s cold in Banff in February.

4. The Practice of Analytics in
Education
UTA’s University Analytics

5. UTA Experience
❖ University Analytics
❖ New University Unit of 25 FTE
❖ Data Scientists for Data Mining, Analytics Across the
Campus Academic and Business Enterprise
❖ Learning Innovation and Networked Knowledge (LINK)
Lab
❖ Research Facility of 20 including Faculty, Staff,
Postdocs, and Graduate-level Researchers

6. Data-Enriched Educational
Products
❖ Online courses that enable the constant logging and
tracking of learners through their clickstream data;
❖ E-textbooks that can ‘learn’ from how they are used;
❖ Adaptive learning systems that enable materials to be
tailored to each student’s individual needs through
automated real-time analysis;

7. As time goes on…
❖ New forms of data analytics that are able to harvest data
from students’ actions, learn from them, and generate
predictions of individual students’ probable future
performances;
❖ Automated personal tutoring software that monitors
students and gives constant real-time support and shapes
the pedagogic experience.
—Mayer-Schönberger & Cukier (2014),
Learning with Big Data: The Future of Education

8. And emerging today…
❖ New forms of data analytics that are able to harvest
data from students’ affective states, social and cognitive
engagement;
❖ More recently: machine learning drives AI tools such as
chatbots, “smart” discussion fora, automated coaching,
etc.
❖ “Smart Campus UTA”

9. Behind it all…
❖ …are models and “training
data” for
❖ personal profiles
❖ e-curriculum pathways
❖ models of student activity,
engagement, affective
states
❖ models for natural-language
interaction with learners

10. What data are feeding our
models?
❖ At UTA, primary sources are our Student Information System (SIS)
and Learning Management System (LMS).
❖ Additional Campus Systems: Student Affairs, Library, Housing
and Food Services
❖ Federation of data from neighboring two-year colleges is/will be
taking place.
❖ Expanding Geographical Context: Arlington and the DFW
Metroplex as “Smart Cities”
❖ Later will add live-stream data from research apps or “wearables.”

11. UA Hardware and Toolsets
❖ Civitas Learning
❖ Multivariate Modeling of Student Persistence, Graduation
❖ IaaS around Student Data
❖ SAS
❖ Visual Analytics
❖ Enterprise Miner
❖ Prediction Suite
❖ Viya Machine Learning/Neural Network Modeling
❖ 450 Core Server Farm (Planned)

12. UTA “Big Data Questions”
❖ How will big data and new models provide a more complex
understanding of the learner in higher education today?
❖ How can universities use big data to improve student success
(retention and successful progress to graduation)?
❖ Can higher education develop new, more multivariate models of
student engagement? How might these models drive faculty, staff,
and coaches to improve student cognitive and social presence in
formal coursework?
❖ How can we better understand learners of diversity and personalize
the educational experience for engagement and success?

14. LMS Data in UTA/Civitas
Model

15. Sample Student Persistence Model
Output

16. SAS Toolset

17. SAS Toolset

18. Learning Analytics Maturity
Model
Siemens, G., Dawson, S., & Lynch, G. (2013

19. New Approach(es)
Cope & Kalantzis (2016), “Big Data Comes to School”

20. New Approach(es)
Cope & Kalantzis (2016), “Big Data Comes to School”

21. NLP Frontier

22. Intercultural Frontier

23. Research of Analytics

24. Projects - Smart Science
Network
$5.2M Bill and Melinda Gates Foundation (Co-PI)
linkresearchlab.org/research

25. BIGDATA: Collaborative
Research
$1.6M NSF (Co-PI)
linkresearchlab.org/research

26. Corporate Partnerships

27. Broadening and expanding the
data inputs for LA
Holistic & Integrated
New tools & techniques
Openness, ethics & scope
Broadening scope of data
Siemens, G. (2012)

28. Working with Santa Fe
Institute
Boeing, NASA, Microsoft, Google

30. Affective and social computing work led by Dr. Catherine
Spann at UTA:

32. Heart Rate Variability
❖ Vagus nerve is the single most important nerve in
the body (Tracey, 2002)
❖ Master regulator: regulates inflammatory
processes, glucose regulation, and hypothalamic-
pituitary-adrenal (HPA) function (Thayer,
Yamamoto, & Brosschot, 2010)
❖ It helps contain acute inflammation and prevents
the spread of inflammation to the bloodstream
❖
Tracey, K. J. (2002). The inflammatory reflex. Nature, 420(6917), 853–859.
Thayer, J. F., Yamamoto, S. S., & Brosschot, J. F. (2010). The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology,
141(2), 122–131. https://doi.org/10.1016/j.ijcard.2009.09.543

33. Heart Rate Variability
❖ Attention and Self-Control (Thayer, 2009)
❖ Supports social engagement (Porges, 2011) and mental well-being
(Kemp & Quintana, 2013)
❖ Important in longer term physical health (Kemp & Quintana, 2013)
❖ Positive emotions (Geisler et al., 2010 ; Oveis et al., 2009)
❖ Psychological flexibility and resilience (Kashdan & Rottenberg, 2010)
❖ Lower HRV associated with depression and anxiety (Kemp, Quintana,
Felmingham, Mathews, & Jelinek, 2012)
❖

34. Current Study:
Self-Control at the Museum
• Methods
• Participants
• Museum visitors
• 7yrs. and older
• Attention and self-control measures
• Dimensional Change Card Sort task
• Self-regulation questionnaire
• Self-Assessment Manikin for mood and arousal
• Physiological data (via E4 wristband)
• Heart rate variability
• Skin conductance
• Accelerometer

35. Psychophysiology
❖ “The body is the medium of experience and the
instrument of action. Through its actions we shape and
organize our experiences and distinguish our
perceptions of the outside world from sensations that
arise within the body itself.” (Miller, 1978, p. 14)

36. Psychophysiology and
learning
Mental effort linked to physiological arousal (Hansen et al.,
2003; Luft et al., 2009)
Mental states influence autonomic nervous system (ANS)