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Using learning analytics to improve student transition into and support throughout the first year

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Presentation supporting the workshop of the STELA and ABLE project at the EFYE 2017 conference in Birmingham.

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Using learning analytics to improve student transition into and support throughout the first year

  1. 1. Using learning analytics to improve student transition into and support throughout the first year Tinne De Laet, Rebecca Edwards, Maartje van den Bogaard, Jan-Paul van Staalduinen
  2. 2. Presentation Overview • Introduction to STELA and ABLE projects • Considerations for using learning analytics • Demonstration of learning analytics dashboards • Reflections on using learning analytics in different contexts
  3. 3. Introduction to the STELA and ABLE Projects
  4. 4. STELA Project • Project partners: • The main goal of the STELA project is to enhance a successful transition from secondary to higher education by means of learning analytics. • The STELA project…  Involves designing and building student and staff facing analytics dashboards  Aims to develop dashboards that go beyond identifying as-risk students; allowing actionable feedback for all students on a large scale STELA Project: 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD www.stela-project.eu @STELA_Project
  5. 5. STELA Project • SECOND STELA SLIDE – TO BE COMPLETED • Detail on STELA project/outputs
  6. 6. ABLE Project • Project partners: • The main goal of the ABLE Project team is to research strategies and practices for using learning analytics to support students during their first year at university. • The ABLE project…  Involves developing the technological aspects of learning analytics  Focuses on how learning analytics can be used to support students ABLE Project: 2015-1-BE-EPPKA3-PI-FORWARD www.ABLEproject.eu @ABLEproject_eu
  7. 7. ABLE Project Outputs • 20 intellectual outputs over the lifecycle of the project
  8. 8. Considerations for using learning analytics
  9. 9. "Analytics is a term used in business and science to refer to computational support for capturing digital data to help inform decision- making … Learning Analytics appropriates this concept for education.” (Buckingham Shum, 2012, p.1) (Clow, 2012) Learning Analytics “Learning analytics uses predictive models that provide actionable information. It is a multidisciplinary approach based on data processing, technology-learning enhancement, educational datamining, and visualization.” (Scheffel, Drachsler, Stoyanov, & Specht, 2014).
  10. 10. Privacy, Ethics and Legal Considerations Student perceptions? Staff perceptions? Student stress? Sophisticated stereotyping? • Complying with the law is vital – but not enough
  11. 11. Operational Considerations Age of university systems? Business owners? Diversity of course structures? Consistency of use? • University operational systems not built/designed with learning analytics in mind
  12. 12. Discussion • Please spend 5 minutes talking to others on the table about the following: 1. What are your core questions about learning analytics? 2. What are your initial thoughts on causes of concern and/or perception of benefits for first year students?
  13. 13. Demonstration of learning analytics dashboards
  14. 14. The NTU Student Dashboard • Can be viewed as two products: Physical Dashboard Algorithm • Staff and students interact with physical dashboard • Algorithm is the behind the scenes, learning analytics element
  15. 15. Core Dashboard Processes Student data Engagement with learning Engagement score Risk alerts Referrals Notes Sense checking Passive useful information Active engagement data High Good Low Partial NFE !
  16. 16. Dashboard TutorStudent Metrics Raw data & engagement rating Student engagement with course Metrics & alerts Engagement with students presented to students students act presented to tutors more-informed interactions Embedded Learning Analytics Two agents of change model
  17. 17. Tutorial Group View Links to group of students in engagement category
  18. 18. Search View Opens link
  19. 19. Links to student’s dashboard Able to sort on headings Search Results View • Designed so staff have easy access to student data. • Allows staff to quickly identify potentially at risk students
  20. 20. Engagement Information View
  21. 21. Student Profile View
  22. 22. Attendance View
  23. 23. Assessment & Feedback View
  24. 24. Notes
  25. 25. Why NTU is interested in learning analytics? Attainment ProgressionBelonging Strategic information Pilots Research
  26. 26. STELA Dashboard • Main part of talk: Describe the STELA Dashboard/s
  27. 27. Reflections on using learning analytics in different contexts
  28. 28. Workshop Activity • On your tables please pick one of the Dashboards to discuss and consider: 1. How might you use the Dashboard to support student transition in your own role? 2. How might the Dashboard work with other initiatives at your institution to support student transition? 3. If you were designing your own learning analytics tool which elements would you keep or enhance and remove or edit?
  29. 29. Discussion • Earlier we spent 5 minutes talking to others on the table about the following: 1. What are your core questions about learning analytics? 2. What are your initial thoughts on causes of concern and/or perception of benefits for first year students? Question: Have your feelings changed? Have we addressed any concerns or raised new ones? Have we unveiled any additional benefits?
  30. 30. Thank you for listening. Any questions? www.ABLEproject.eu @ABLEproject_eu www.stela-project.eu @STELA_Project

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