Keynote LACE: Learning Analytics Community Exchange
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Woensdag 11 november Keynote Titel: LACE: Learning Analytics Community Exchange Spreker(s): Doug Clow Zaal: Rotterdam Hall
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Keynote LACE: Learning Analytics Community Exchange
- 1. Learning Analytics: Trains and Balloons Dr Doug Clow Institute of Educational Technology, The Open University, UK @dougclow dougclow.org doug.clow@open.ac.uk
- 2. 2 2 CC-BY – You are free to: copy, share, adapt, or re-mix; photograph, film, or broadcast; blog, live-blog, or post video of this presentation provided that: You attribute the work to its author and respect the rights and licences associated with its components. www.slideshare.net/dougclow
- 3. 3 Photo (CC)-BY Alexandre Delbos https://flic.kr/p/xthYs • What is learning analytics? • Train learning & balloon learning • Examples • What next?
- 4. What is learning analytics? • the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs – First International Conference on Learning Analytics And Knowledge (LAK11), Banff, Alberta, Feb 27- Mar 1, 2011 Photo (CC)-BY Cris http://flickr.com/photos/chrismatos/6917786197/
- 5. 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”
- 7. train learning & balloon learning
- 8. 8 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks Balloon v train story
- 9. 9 GoGo Board GoGo Board photo © TLTL, Stanford, https://tltl.stanford.edu/projects/gogoboard/
- 10. 10
- 11. 11 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks
- 12. 12 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks Balloon v train story
- 13. 13 Photo (CC)-BY-SA Ami Photography https://flic.kr/p/pxTUks Balloon v train story
- 14. 14 Photo (CC)-BY-SA Grant https://flic.kr/p/oaaKbD
- 15. Which group learned? How could you tell?
- 16. 16 Photo (CC)-BY-SA Dudva https://commons.wikimedia.org/wiki/File:Number_4468_Mallard_in_York.jpg
- 17. 17 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
- 18. 18 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
- 19. 19 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761
- 20. 20 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761 • Conceptual change • Learner led • Tacit knowledge • Projects • Problem-solving • Expansive • Wildfire • Co-construction • Relationships • Teamwork
- 21. 21 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
- 22. Which way should we go?
- 23. 23Tesla factory in Fremont, CA Photo © Paul Sakuma/Associated Press
- 24. 24 Will a robot take your job? http://bbc.in/1Q5Vecw
- 27. 27Tesla factory in Fremont, CA Photo © Paul Sakuma/Associated Press • Social intelligence • Creativity • Perception & manipulation Frey & Osborne (2013)
- 28. 28 Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761
- 30. Dashboards
- 31. School dashboards (Google Images) • Maybe chop the first slide about this. 31
- 32. 32Class 142 cab Photo (CC)-BY-NC-ND Roger Marks https://flic.kr/p/7gSP1t
- 33. 33Concorde cockpit Photo (CC)-BY-SA Sam https://flic.kr/p/cqAVej
- 34. Collect the data you want
- 35. 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 35 Buckingham Shum and Deakin Crick, 2012 (LAK12)
- 36. 36 ELLI, Buckingham Shum and Deakin Crick, 2012 (LAK12) Photo (CC)-BY HiggySTFC https://flic.kr/p/3hp761
- 37. Keep humans in your system
- 38. 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.” 38
- 39. 39 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.
- 40. 40 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.
- 41. The difference is small
- 42. What is the next step?
- 43. www.laceproject.eu Learning Analytics Community Exchange (FP7) • Coordination and Support • Evidence Hub • Events • Publications, briefings, webinars
- 44. Thanks to: People: • LACE at the OU: Rebecca Ferguson, Andrew Brasher, Bart Rientes, Simon Cross, Linda Norwood Michelle Bailey, Rebecca Wilson, Evaghn De Souza, Natalie Eggleston, Oliver Millard, Gary Elliot-Citigottis. • LACE project partners: CETIS (Bolton), OUNL, Skolverket, HIOA, Kennisnet, ITS, ATiT. • The learning analytics community, including SoLAR, IEDMS, LAK and LASI • Kennisnet for inviting me Funders: • LACE: European Commission 619424-FP7-ICT-2013-11
- 45. 45 Photo (CC)-BY-NC-ND warnerphotography https://flic.kr/p/5SvZmi
- 46. “Learning Analytics: Train and Balloons” by Doug Clow, Institute of Educational Technology, The Open University, UK was presented at Dé Onderwijsdagen, Rotterdam, 11 November 2015. @dougclow dougclow.org doug.clow@open.ac.uk This work was undertaken as part of the LACE Project, supported by the European Commission Seventh Framework Programme, grant 619424. These slides are provided under the Creative Commons Attribution Licence: http://creativecommons.org/licenses/by/4.0/. Some images used may have different licence terms. www.laceproject.eu @laceproject 46
- 47. cc licensed ( BY ) flickr photo by David Goehring: http://flickr.com/photos/carbonnyc/33413040/
- 48. 48 Photo © Google https://www.google.com/selfdrivingcar/where/
- 49. M4 Motorway Cameras 49 Photo (CC)-BY Phillip Williams http://www.geograph.org.uk/photo/1342357
Notas del editor
- Please copy, adapt, photograph, video. Tell your friends!
- Hendrik Drachsler, Niall Sclater
- Learning analytics is on the Internet and the Internet has cats Data mining, business intelligence, academic analytics, learner analytics – focus here is on the learning, not the management and administration of learning
- Big Data in education More learning online. More sensors making offline, online.
- Many definitions. Slippery. I like ‘Changed capacity to act.’
- Stanford, Paulo Blikstein’s lab
- 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.
- Promised you some examples!
- Promised you some examples!
- Your school will have software with dashboards, with VLE/LMS data. Get hold of it! Dashboard as a metaphor
- 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!
- Don’t just use 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
- Big thanks in small fonts
- 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!