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Co-designing learning dashboards for scalable feedback

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Slides used during the workshop at the LALA conference, Guayaquil, Ecuador on 10 July 2018.

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Co-designing learning dashboards for scalable feedback

  1. 1. Co-designing learning dashboards for scalable feedback Tom Broos & Tinne De Laet @TomBroos @TinneDeLaet
  2. 2. largest university in Belgium founded 1425 16 faculties → general university  55 000 students  tuition fee for 1 year < 1 3 average monthly income
  3. 3. “Learning analytics is about collecting traces that learners leave behind and using those traces to improve learning.” - Erik Duval Learning Analytics and Educational Data Mining, Erik Duval’s Weblog, 30 January 2012, 3 Learning Analytics?
  4. 4. Learning Dashboards? 4Dashboard Confusion, Stephen Few, Intelligent Enterprise, March 20, 2004 “A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance.” - Stephen Few
  5. 5. Successful Transition from secondary to higher Education using Learning Analytics enhance a successful transition from secondary to higher education by means of learning analytics  design and build analytics dashboards,  dashboards that go beyond identifying at-risk students, allowing actionable feedback for all students on a large scale. Achieving Benefits from Learning Analytics research strategies and practices for using learning analytics to support students during their first year at university  developing the technological aspects of learning analytics,  focuses on how learning analytics can be used to support students. 10 @STELA_project 2015-1-UK01-KA203-013767 @ABLE_project_eu 562167-EPP-1-2015-1-BE-EPPKA3-PI-FORWARD
  6. 6. STELA ♥ ABLE 11 actionable feedback student-centered program level inclusive first-year experience institution-wide Learning Analytics actual implementation
  7. 7. [!] Feedback must be “actionable”. 12 Warning! Male students have 10% less probability to be successful. You are male. Warning! Your online activity is lagging behind. action? ? action? ? 
  8. 8. learning dashboards @KU Leuven interaction self-reflection LISSA STUDENT ADVISOR STUDENT LASSI – learning skills REX - scoresPOS – future students
  9. 9. [!] Start with the available data. Lots of data may eventually become available in the future … …. already start with what is available 14 (*) (*) Zarraonandia, T., Aedo, I., Díaz, P., & Montero, A. (2013). An augmented lecture feedback system to support learner and teacher communication. British Journal of Educational Technology, 44(4), 616-628.
  10. 10. [!] Involve stakeholder expertise. 16 visualization experts practitioners / end-users researchers LA researchers first-year study success Charleer S., Vande Moere A., Klerkx J., Verbert K., De Laet T. (2017). Learning Analytics Dashboards to Support Adviser-Student Dialogue. In IEEE Transactions on Learning Technology (
  11. 11. Grade data two dashboards: LISSA & REX
  14. 14. ETL LA- DWH JSONAPI cache
  15. 15. LISSA dashboard interaction student – study advisor
  16. 16. Study advisor – student conversations 23 Should I consider another program? Can I still finish the bachelor in 3 years? How should I compose my program for next year? What is the personal situation? How can I help? What is the best next step?
  17. 17. LISSA dashboard
  18. 18. LISSA: status 25 26 programs >4500 students 114 student advisors training of study advisors • Charleer S., Vande Moere A., Klerkx J., Verbert K., De Laet T. (2017). Learning Analytics Dashboards to Support Adviser-Student Dialogue. In IEEE Transactions on Learning Technology • • Millecamp M., Gutiérrez F., Charleer S., Verbert K., De Laet T.# (2018). A qualitative evaluation of a learning dashboard to support advisor-student dialogues. Proceedings of the 8th International Learning Analytics & Knowledge Conference. LAK. Sydney, 5-9 March 2018 (pp. 1-5) ACM. dashboards for three examination periods observations, interviews, questionnaires
  19. 19. How to determine thresholds for different groups? Stakeholder involvement: example 1 26  upper and lower group: clear message  middle group as small as possible  Do not overfit! (nuance)
  20. 20. Evaluation – interviews “When students see the numbers, they are surprised, but now they believe me. Before, I used my gut feeling, now I feel more certain of what I say as well”. “It’s like a main thread guiding the conversation.” “I can talk about what to do with the results, instead of each time looking for the data and puzzling it together.” “Students don’t know where to look during the conversation, and avoid eye contact. The dashboard provides them a point of focus”. “A student changed her study method in June and could now see it paid off.” LISSA supports a personal dialogue.  the level of usage depends on the experience and style of the study advisors  fact-based evidence at the side  narrative thread  key moments and student path help to reconstruct personal track “I can focus on the student’s personal path, rather than on the facts.” “Now, I can blame the dashboard and focus on collaboratively looking for the next step to take.” 27
  21. 21. [!] Do not oversimplify. Show uncertainty. 28 • reality is complex • measurement is limited • individual circumstances • need for nuance • trigger reflection
  22. 22. REX student-facing dashboard
  23. 23. [!] Start with the available data. 30 data already available? administrative (examples) student records course grades systems (examples) LMS access logs advisor meetings ) Broos T., Verbert K., Van Soom C., Langie G., De Laet T.# (2018). Small data as a conversation starter for learning analytics: exam results dashboard for first-year students in higher education. Journal of Research in Innovative Teaching & Learning, , 1-14. demo: (en) or (nl)
  24. 24. Stakeholder involvement: example 2 31
  25. 25. Stakeholder involvement: example 2 32
  26. 26. Stakeholder involvement: example 2 33 Student is starter? Study efficienc y? Mail invitation Student visits dashboard Reminder LOW GLOBAL SE TIPS REGULATIONS MID GLOBAL SE HIGH GLOBAL SE ADVICE INTRODUCTION MAIL REMINDER SCORE
  27. 27. Stakeholder involvement: example 2 34
  28. 28. Evaluation - REX 35
  29. 29. What can we learn from dashboard usage? 36Broos T., Verbert K., Van Soom C., Langie G., De Laet T.# (2018). Low-investment, Realistic-Return Business Cases for Learning Analytics Dashboards: Leveraging Usage Data and Microinteractions. accepted for ECTEL 2018 p<1e-5 p<1e-9p-test
  30. 30. Design your own grading dashboard
  31. 31. Design your own considerations • data • stakeholders • textual and visual elements • incorporating in university practices 39 inspiration
  32. 32. [!] Context matters! • available data • national and institutional regulations and culture • educational vision • educational system, size of population .. • … Don’t just copy existing LA solutions! 40
  33. 33. Project team @ 41 Sven Charleer AugmentHCI, Computer Science department PhD researcher ABLE Katrien Verbert AugmentHCI, Computer Science department Copromotor of STELA & ABLE Carolien Van Soom Leuven Engineering and Science Education Center Head of Tutorial Services of Science Copromotor of STELA & ABLE Greet Langie Leuven Engineering and Science Education Center Vicedean (education) faculty of Engineering Technology Copromotor of STELA & ABLE Tinne De Laet Leuven Engineering and Science Education Center Head of Tutorial Services of Engineering Science Coordinator of STELA KU Leuven coordinator of ABLE Francisco Gutiérrez AugmentHCI, Computer Science department PhD researcher ABLE Tom Broos Leuven Engineering and Science Education Center AugmentHCI, Computer Science department PhD researcher STELA Martijn Millecamp AugmentHCI, Computer Science department PhD researcher ABLE Special thanks to study advisors for their cooperation, advice, feedback, and support! Jasper, Bart, Riet, Hilde, An, Katrien, … ♥