Trains and Balloons: An Introduction to Learning Analytics
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Slides for a talk given at the Institute of Physics Higher Education Group meeting on Concept Inventories and Learning Analytics, Tue 4 April 2017, Open University, UK
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Trains and Balloons: An Introduction to Learning Analytics
- 1. Trains and Balloons: An Introduction to Learning Analytics Doug Clow, Institute of Educational Technology The Open University HE Group meeting #IOPCI17 Open University, 4 April 2017
- 2. These slides (CC)-BY Doug Clow slideshare.net/dougclow 2
- 3. 1) learning analytics 2) train learning & balloon learning 3) your journey 3Photo (CC) BY-NC-ND Ross G. Strachan https://flic.kr/p/2NUYKW
- 5. Learning analytics … … the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. solaresearch.org 5
- 6. Cloud Chamber at the German Electron Synchrotron DESY Photo public domain: http://commons.wikimedia.org/wiki/File:DESYNebelkammer.jpg - Erik Duval http://erikduval.wordpress.com/2012/01/30/learning- analytics-and-educational-data-mining/ “collecting traces that learners leave behind and using those traces to improve learning”
- 9. 2) train learning & balloon learning
- 10. 10 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks Balloon v train story
- 11. 11 GoGo Board GoGo Board photo © TLTL, Stanford, https://tltl.stanford.edu/projects/gogoboard/
- 12. 12
- 13. 13 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks
- 14. 14 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks Balloon v train story
- 15. 15 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks Balloon v train story
- 16. 16 Photo (CC)-BY-SA Grant https://flic.kr/p/oaaKbD
- 17. Which group learned? How could you tell?
- 18. 18 Photo (CC)-BY-SA Dudva https://commons.wikimedia.org/wiki/File:Number_4468_Mallard_in_York.jpg
- 19. 19 Photo (CC)-BY-SA Dudva https://commons.wikimedia.org/wiki/File:Number_4468_Mallard_in_York.jpg • Curriculum coverage • Teacher expertise • Explicit knowledge • Mastery • Clarity • Planning • Accountability • Safety • Reliability
- 20. 20 Photo (CC)-BY-SA Dudva https://commons.wikimedia.org/wiki/File:Number_4468_Mallard_in_York.jpg • Curriculum coverage • Teacher expertise • Explicit knowledge • Mastery • Clarity • Planning • Accountability • Safety • Reliability • Instructivist • Information transmission • Teacher control • Rote learning • Surveillance • Boring
- 21. 21 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761
- 22. 22 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761 • Conceptual change • Learner led • Tacit knowledge • Projects • Problem-solving • Expansive • Wildfire • Co-construction • Relationships • Teamwork
- 23. 23 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761 • Conceptual change • Learner led • Tacit knowledge • Projects • Problem-solving • Expansive • Wildfire • Co-construction • Relationships • Teamwork • Time-consuming • Unclear • Open-ended • Hard to assess • Ungrounded
- 24. Which way should we go?
- 25. 25Tesla factory in Fremont, CA Photo © Paul Sakuma/Associated Press
- 26. 26 Will a robot take your job? http://bbc.in/1Q5Vecw
- 29. 29Tesla factory in Fremont, CA Photo © Paul Sakuma/Associated Press •Social intelligence •Creativity •Perception & manipulation Frey & Osborne (2013)
- 30. 30 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761
- 31. 3) your journey
- 32. Your journey: a) Where are you now? b) Where do you want to get to? c) How will you get there?
- 33. Your journey: a) Where are you now? b) Where do you want to get to? c) How will you get there?
- 34. What learning data can you get hold of? Discuss in your tables 34
- 35. Your journey: a) Where are you now? b) Where do you want to get to? c) How will you get there?
- 36. dashboards
- 37. 37 University Dashboard (Google Image search)
- 38. 38Class 142 cab Photo (CC)-BY-NC-ND Roger Marks https://flic.kr/p/7gSP1t
- 39. 39Concorde cockpit Photo (CC)-BY-SA Sam https://flic.kr/p/cqAVej
- 40. collect the data you want
- 41. Dispositions analytics ● Learning dispositions ● ‘Learning power’: Effective Lifelong Learning Inventory ● “A framework for a coaching conversation which moves between identity and performance” ● Schools to Graduate schools 41 Buckingham Shum and Deakin Crick, 2012 (LAK12)
- 42. 42 ELLI, Buckingham Shum and Deakin Crick, 2012 (LAK12) Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761
- 43. keep humans in your system
- 44. Image (cc) Darwin Bell http://www.flickr.com/photos/darwinbell/296553221/ “The predictive model is used as a trigger for intervention emails to the learner.” 44
- 45. 45 Photo (CC)-BY-SA Dudva https://commons.wikimedia.org/wiki/File:Number_4468_Mallard_in_York.jpg From: DONOTREPLY@mail.example.com You are in trouble. The computer predictive model gives you a 87.4322% chance of failing this course.
- 46. 46 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761 Hi Alex Are you Ok? I noticed you haven’t logged on this week, and I know you struggled with the last assessment. We can work through this together - let’s have a chat as soon as possible. Pat.
- 47. the difference is small
- 48. Your journey: a) Where are you now? b) Where do you want to get to? c) How will you get there?
- 49. What do you want to do with learning analytics? Discuss in your tables 49
- 50. Your journey: a) Where are you now? b) Where do you want to get to? c) How will you get there?
- 51. 51 • Review of current practice in UK HE & FE • Code of practice for learning analytics • Evidence, Literature review • Readiness questionnaire (onboarding guide) • Learning analytics service https://analytics.jiscinvolve.org/ Effective Learning Analytics
- 53. •LAK conferences •LASI workshops •Flare local meetings •Storm PhD training •Journal of LA •… and more! www.solaresearch.org
- 55. www.laceproject.eu • Learning Analytics Review bit.ly/lace-review-papers • DELICATE Ethics & Privacy Checklist bit.ly/lace-ethics • Evidence Hub evidence.laceproject.eu • Learning Analytics Community Europe
- 56. Your journey: a) Where are you now? b) Where do you want to get to? c) How will you get there?
- 57. What’s the next step? Discuss in your tables 57
- 58. Test what you do • Does it work? • You’ll have data! Photo (CC)-BY Kevin Dooley https://www.flickr.com/photos/pagedooley/6613526021/ Towards Evidence- Based Practice
- 59. 59 Organisations are built to resist change • Rock and the river? • Big lever Vernal Falls, Yosemite Photo CC BY Mary PK Burns https://flic.kr/p/6zUzqh
- 60. 60 Photo (CC)-BY-NC-ND warnerphotography https://flic.kr/p/5SvZmi
- 62. cc licensed ( BY ) flickr photo by David Goehring: http://flickr.com/photos/carbonnyc/33413040/
- 63. 63 Photo © Google https://www.google.com/selfdrivingcar/where/
- 64. M4 Motorway Cameras 64 Photo (CC)-BY Phillip Williams http://www.geograph.org.uk/photo/1342357
- 66. What data do we have about learners? • Demographics • Previous educational experience • Grades, scores, achievements, struggles • Attendance, location, gaze • Software logs • Online tracking • Other online activity (tracking) • … more every week. 66 Photo (CC)-BY-SA AJ Cann https://www.flickr.com/photos/ajc1/15574010080/
- 67. What can we do with that data? • Identify learners who need help – Simple or predictive • Trigger interventions – Via teacher, or direct • Learn which interventions work • Build a complete cognitive learning system • Suggest resources or source of help – Learners like you found this helpful – This person might be able to help you 67 Photo (CC)-BY-NC Pulpolux https://www.flickr.com/photos/pulpolux/8735428280
- 68. 68 Greller & Drachsler (ET&S, 2012)
Editor's Notes
- Tour: I will try to show you the main sights, but I am a not unbiased. MOSTLY WHITE MEN
- Many definitions. Slippery. I like ‘Changed capacity to act.’ Optimising learning!
- Big Data in education More learning online. More sensors making offline, online.
- Without interventions: still good stuff: computer science, educational research, business intelligence But only LA if fed back. What good teachers have always been doing, but more data, and better techniques.
- What are you optimising? Assessment crucial Optimising out.
- Many definitions. Slippery. I like ‘Changed capacity to act.’ Optimising learning!
- Laser cutter Followed instructions on the sheet Load the shape file, change, load plastic in to machine, cut shapes. Glue.
- Sensors: temperature, sound, light. Outputs: motors, LEDs, display Program board to control. Three groups. Input a signal, output a signal.
- Train – assess output, learned to use laser cutter. Balloon – harder. Softer skills. But multimodal data. Two visions of learning.
- Train learning
- If we’re making our lives easier, teachers’ lives easier, use the space for better learning
- If we’re making our lives easier, teachers’ lives easier, use the space for better learning
- Balloon learning
- Balloon learning
- Balloon learning
- Neither is better
- Robots are taking our jobs!
- There are loads of studies/papers/arguments that robots will take our jobs. Not just assembly line workers.
- What is hard to automate? Soc intel: dishwasher vs PR Creativity: legal clerk vs biologist Manipulation: telesales vs surgeon
- Balloon view is the future. Train is the pressure now. Must do it, so do it. But look up! Make space.
- Many definitions. Slippery. I like ‘Changed capacity to act.’ Optimising learning!
- Data Warehouse, pilot projects, VLE stats, dashboards? Who has it? Deanery, IT / Computing Service, Registry, Library Talk to neighbour, one minute each way, I’ll referee.
- Everybody wants a dashboard
- Almost every Univ s/w product has a dashboard or analytics Illusion of control and mastery Vs Making right data visible to people who can do something about it Dashboard analogy: keep looking out the window. Cruise control vs look at speedo vs appropriate speed Doesn’t tell you where you should go!
- Class 142 cab. Very simple
- Concorde cockpit. Much harder. Engineer’s block to the right. Don’t use the data just because you have it. Look out of the window!
- Not just the data you’re given
- Cohort dispositional analytics. Building critical self-awareness. Correlations with success measures, but complex relationship. Learning power goes down over time in school!
- Do not reply. Computer. Will fail. Self-fulfilling. Have seen worse! At least it’s clear what it’s saying.
- Human relationship You are monitored vs I noticed
- M4 motorway – share your data with students
- Tell your neighbour. One minute each way, I’ll referee. Big procurement exercise for an analytics infrastructure, data warehouse plus viz/analytics suite To running a small exercise in a couple of your lectures (starting small is Ok) Developing an institutional analytics strategy To making sure students get their marks on time
- Niall Sclater
- Niall Sclater
- LASI UMich
- Interviews with LA experts, and me
- We can help!
- Talk to your neighbour, two minutes each way. It could be a tiny thing: see what analytics your VLE can produce already, It could be big: Develop an internal funding pitch for a comprehensive analytics plan Make it concrete and specific. Make the first step do-able today or tomorrow morning.
- Idealised You’re scientists!
- Organisations are built to resist change Solid granite, wears away over time
- Do both. Train and balloon.
- This is Google’s self-driving car! If you can automate it, automate it, and use the time for balloon learning. Computer programming.
- M4 is a motorway that goes from London, England, all the way to South West Wales. Wales is a different country from England. Separate, like Scotland. Roads are managed by a different organisation. In England, surveillance. Who is looking at me? Why are they looking at me?
- In England, surveillance. Who is looking at me? Why are they looking at me? In Wales, cameras visible online. I can see when a junction is busy. Now I feel sorry for the person who has to watch all these cameras for traffic jams. Transparency helps. And Traffic England have now done the same!
- To help the learner! Not just tracking.
- Review.