Bring your own idea - Visual learning analytics

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Workshop on visual learning analytics that was part of LASI 2014 - http://www.solaresearch.org/events/lasi-2/lasi2014/

Examples of learning dashboards were presented during the workshop by Sven Charleer:
http://www.slideshare.net/svencharleer/learning-dashboard-visual-learning-analytics-workshop-lasi2014-h-harvard

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Bring your own idea - Visual learning analytics

  1. 1. Visual learning analytics Joris Klerkx Research Expert, PhD. @jkofmsk Sven Charleer Phd candidate @svencharleer Erik Duval Professor @erikduval http://www.slideshare.net/jkofmsk
  2. 2. Our team
 HCI lab ! technology enhanced le music research (personal) health
  3. 3. 3 http://eng.kuleuven.be/datavislab/
  4. 4. About you… Why are you interested in this workshop?
  5. 5. Agenda (more or less) • BEFORE THE BREAK: • Information visualization (theory) • Group work - Design & Sketch your first visualizations • AFTER THE BREAK: • (Visual) Learning Analytics Dashboards • Tips `n tricks • Group work - Design your own learning analytics dashboard
  6. 6. WHAT?
  7. 7. http://www.slideshare.net/infoscape
  8. 8. Information Visualisation is the use of interactive visual representations to amplify cognition [Card. et. al]
  9. 9. Anscombe`s quartet ! uX = 9.0 uY = 7.5 sigma X = 3.317 sigma Y = 2.03 Y = 3 + 0.5X Discover patterns in the data http://en.wikipedia.org/wiki/Anscombe's_quartet
  10. 10. Tell the story behind the data Will there be enough food? Communicate data http://www.footprintnetwork.org/en/index.php/gfn/page/earth_overshoot_day/
  11. 11. http://infosthetics.com/ http://visualizing.org http://www.visualcomplexity.com/vc/ http://visual.ly/ http://flowingdata.com http://www.infovis-wiki.net http://datastori.es/ http://helpmeviz.com/ …
  12. 12. (Just enough) THEORY
  13. 13. How many circles?
  14. 14. Humans have advanced perceptual abilities Our brains makes us extremely good at recognizing visual patterns
  15. 15. ¡ Law of Symmetry Objects must be balanced or symmetrical to be seen as complete or whole (Chang, 2002). Gestalt Principles http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation ¡ Law of Proximity The closer objects are to each other, the more likely they are to be perceived as a group (Ehrenstein, 2004)
  16. 16. ¡ Law of Similarity
 Objects that are similar, with like components or attributes are more likely to be organised together (Schamber, 1986). Objects are viewed in vertical rows because of their similar attributes. ¡ Law of Common Fate
 Objects with a common movement, that move in the same direction, at the same pace , at the same time are organised as a group (Ehrenstein, 2004). Gestalt Principles http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
  17. 17. ¡ Law of Continuation
 Objects will be grouped as a whole if they are co- linear, or follow a direction (Chang, 2002; Lyons, 2001). ¡ Law of Isomorphism ! Is similarity that can be behavioural or perceptual, and can be a response based on the viewers previous experiences (Luchins & Luchins, 1999; Chang, 2002). This law is the basis for symbolism (Schamber, 1986). There are many more!http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation Gestalt Principles
  18. 18. Which visual encodings do you see? London Tube Map
  19. 19. http://artspilesenglish.blogspot.be/2011/11/gestalt-theory-exercise-for-3rdlevel.html http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
  20. 20. A limited set of visual properties that are detected very rapidly (< 250 ms) in multi-element display and accurately by the low-level visual system. Pre-attentive characteristics Find the red dot <> Hue Find the dot <> shape Find the red dot conjunction not pre-attentive http://www.csc.ncsu.edu/faculty/healey/PP/
  21. 21. Pre-attentive characteristics Line orientation Length, width Closure Size Curvature Density, contrast Intersection 3D depth
  22. 22. Do not help with showing exact quantitative differences Pre-attentive characteristics help to spot differences in multi-element display E.g. size & radius
  23. 23. How to start your visualization? Data set Visualisation
  24. 24. Step 1. Get to know your data Time? hierarchical? 1D? 2D? nD? network? … Quantitive, ordinal, categorical? S. Stevens “On the theory of scales and measurements” (1946)
  25. 25. What is the average amount of students that bought the course book ? Step 2. Formulate questions about your data What? When? How much? How often? (why?) When did students start looking at the course material? How much hours did Peter work on this assignment? (Why did Peter have to redo his assignment?) How often did Peter retake the course before he passed?
  26. 26. Encode data characteristics into visual form Step 3: Apply a visual mapping Simplicity is the ultimate sophistication. Leonardo da Vinci Each mark (point, line, area,…) represents a data element Think about relationships between elements (position)
  27. 27. Find all possible ways to visualize a small data set of two numbers { 75, 37 } http://blog.visual.ly/45-ways-to-communicate-two-quantities/ +/- 15 minutes Small groups - sketch EXERCISE
  28. 28. Learning analytics 31
  29. 29. Collecting traces that learners leave behind and using those traces to improve learning http://erikduval.wordpress.com/2012/01/30/learning-analytics-and-educational-data-mining/ Learning analytics 32
  30. 30. What to measure? Depends on the user 33
  31. 31. Example traces of Students access to learning resources
 posts in discussion fora
 logins to learning management systems
 posts of assignments
 replies to posts
 votes in lecture response systems
 time on page in electronic textbook
 location of device used to access course
 (and thus proximity to other users)
 software lines produced
 contributions to shared documents or wikis etc. Who? ! ! What? ! ! When? 34
  32. 32. Analytics for professors 35
  33. 33. email, twitter, facebook, web reading, physical movement, location, proximity, food intake, sleeping, drinking, emotion tracking, weather info, attention, brainwaves, … As learning moves online, traces also include… 36
  34. 34. EXERCISE 1. Brainstorm about a learning analytics data set ! Choose +/- 5 types of user traces 2. Get to know this data ! Time? hierarchical? Quantitative? Categorical? … 3. What questions do you have about this data ! what? when? How much? etc. 4. Apply a visual mapping ! Marks, position, color, shape, gestalt principles, pre-attentive characteristics
  35. 35. Sketch
  36. 36. BREAK
  37. 37. LEARNING DASHBOARDS (SVEN)
  38. 38. Tips `n tricks
  39. 39. Real data is ugly and needs to be cleaned http://www.netmagazine.com/features/seven-dirty-secrets-data-visualisation https://code.google.com/p/google-refine/ http://vis.stanford.edu/wrangler/ Pre-process your data http://hcil2.cs.umd.edu/trs/2011-34/2011-34.pdf
  40. 40. Forget about 3D graphs Occlusion Complex to interact with Doesn’t add anything
  41. 41. Size & angle are not pre-attentive: difficult to compare Limited Short term (visual) memory Save the pies for dessert (S.Few) Which student has more blogposts?
  42. 42. 0" 5" 10" 15" 20" 25" 30" blogposts" tweets" comments"on" blogs" reports" submi6ed" Student'1' Student"1" 0" 5" 10" 15" 20" 25" 30" blogposts" comments"on" blogs" tweets" reports" submi6ed" Student'1' Student"1" Use common sense 0" 5" 10" 15" 20" 25" 30" blogposts" comments"on"blogs" tweets" reports"submi6ed" Student'1' Student"1"
  43. 43. 0" 10" 20" 30" 40" 50" 60" Student"1" Student"2" Student"3" Student"4" blogposts" tweets" comments"on"blogs" reports"submi:ed" 0%# 20%# 40%# 60%# 80%# 100%# Student#1# Student#2# Student#3# Student#4# blogposts# tweets# comments#on#blogs# reports#submi;ed# What/how are you comparing? What story do you get from it? Use common sense
  44. 44. http://www.perceptualedge.com/ Which graph makes it easier to focus on the pattern of change through time, instead of the individual values? Choose graph that answers your questions about your data
  45. 45. http://flowingdata.com/category/statistics/mistaken-data/ BP - leak in gulf of mexico Don`t use misleading visualizations
  46. 46. Don`t use visualizations to lie... http://www.perceptualedge.com/ http://flowingdata.com/category/statistics/mistaken-data/
  47. 47. http://flowingdata.com/category/statistics/mistaken-data/http://flowingdata.com/category/statistics/mistaken-data/ Don`t use visualizations to lie...
  48. 48. Humans have little short term (visual) memory Our brain remembers relatively little of what we perceive Humans have advanced perceptual abilities Our brains makes us extremely good at recognizing visual patterns Interaction techniques and visual cues can help
  49. 49. http://www.youtube.com/watch?v=OVlJv7ZkvGA
  50. 50. http://queue.acm.org/detail.cfm?id=2146416
  51. 51. EXERCISE 1. Reiterate over previous visual mappings ! Incorporate feedback, lessons learned 2. Walk around and present your work to each other !

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