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Learning Analytics - George Siemens

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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.

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Learning Analytics - George Siemens

  1. 1. Learning Analytics: its emergence, trends, and systemic George Siemens University of Michigan January 23, 2017
  2. 2. Emergence ❖ A quick background: ❖ LAK11 ❖ LASI ❖ JLA ❖ Organizational structure ❖ Also, it’s cold in Banff in February.
  3. 3. The Practice of Analytics in Education UTA’s University Analytics
  4. 4. 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
  5. 5. 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;
  6. 6. 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
  7. 7. 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”
  8. 8. 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
  9. 9. 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.”
  10. 10. 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)
  11. 11. 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?
  12. 12. LMS Data in UTA/Civitas Model
  13. 13. Sample Student Persistence Model Output
  14. 14. SAS Toolset
  15. 15. SAS Toolset
  16. 16. Learning Analytics Maturity Model Siemens, G., Dawson, S., & Lynch, G. (2013
  17. 17. New Approach(es) Cope & Kalantzis (2016), “Big Data Comes to School”
  18. 18. New Approach(es) Cope & Kalantzis (2016), “Big Data Comes to School”
  19. 19. NLP Frontier
  20. 20. Intercultural Frontier
  21. 21. Research of Analytics
  22. 22. Projects - Smart Science Network $5.2M Bill and Melinda Gates Foundation (Co-PI) linkresearchlab.org/research
  23. 23. BIGDATA: Collaborative Research $1.6M NSF (Co-PI) linkresearchlab.org/research
  24. 24. Corporate Partnerships
  25. 25. Broadening and expanding the data inputs for LA Holistic & Integrated New tools & techniques Openness, ethics & scope Broadening scope of data Siemens, G. (2012)
  26. 26. Working with Santa Fe Institute Boeing, NASA, Microsoft, Google
  27. 27. Affective and social computing work led by Dr. Catherine Spann at UTA:
  28. 28. 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
  29. 29. 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) ❖
  30. 30. 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
  31. 31. 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)
  32. 32. Psychophysiology and learning Mental effort linked to physiological arousal (Hansen et al., 2003; Luft et al., 2009) Mental states influence autonomic nervous system (ANS)

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