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“The most important single factor
influencing learning is what the learner
already knows. Ascertain this and teach
[them] accordingly.”
– David Ausubel, 1968](https://image.slidesharecdn.com/2014-10skolverket-dougclow-141010031508-conversion-gate02/85/Learning-Analytics-A-General-Introduction-and-Perspectives-from-the-UK-7-320.jpg)
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Learning Analytics: A General Introduction and Perspectives from the UK
The document is a presentation by Dr. Doug Clow from the Open University discussing learning analytics, which involves the measurement and analysis of data concerning learners to optimize their learning experiences in the UK educational context. It highlights the role of predictive modeling in identifying at-risk students and the importance of ethics, privacy, and transparency in using educational data. The presentation also emphasizes the collaborative nature of learning analytics initiatives and the commitment to improving educational outcomes through data-driven insights.
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Learning Analytics: A General Introduction and Perspectives from the UK
- 1. Dr Doug Clow Instituteof Educational Technology, The Open University, UK @dougclow dougclow.org doug.clow@open.ac.uk Læringsanalyse: en allmän introduktion och perspektiv från Storbritannien
- 2. Learning Analytics: A GeneralIntroduction & Perspectives from the UK Dr Doug Clow Institute of Educational Technology, The Open University, UK @dougclow dougclow.org doug.clow@open.ac.uk
- 3. 3 3 You are freeto: 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.
- 4. Using Lanyrd The Lanyrdsocial directory of events provides access to information about: • Events • Speaker • Participants The entry for this event may include access to: • Slides • Twitter archives • Reports Feel free to add your details 4 http://bit.ly/1v0XT05
- 5. 1. learning analytics 2.at the Open University 3. in UK schools
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- 7. Photo (CC)-BY-NC-SA tim_dhttps://www.flickr.com/photos/tim_d/184018928 7 “The most important single factor influencing learning is what the learner already knows. Ascertain this and teach [them] accordingly.” – David Ausubel, 1968
- 8. What is learninganalytics? • 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/
- 9. 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”
- 10. “feeding back the dataexhaust” Big Data in Education Photo (CC)-BY Iain Watson http://www.flickr.com/photos/dagoaty/3329699788/
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- 12. (cc) Doug Clowhttp://dougclow.org
- 13. • Predictive modeling –Datamining, Blackboard • Place students in one of three risk groups – traffic light / signal / robot • Trigger for intervention emails • Dramatic retention improvements • Consistent grade performance improvement
- 14. 14 control Photo (CC)-BY AndyRoberts https://www.flickr.com/photos/aroberts/3035796 surveillance
- 15. 15 support Photo (CC)-BY-NC-SA DrewBennett https://www.flickr.com/photos/abennett96/2710211041 guidance
- 16. M4 Motorway Cameras 16 Diagram(CC0) http://en.wikipedia.org/wiki/File:British_Isles_Euler_diagram_15.svg
- 17. M4 Motorway Cameras 17 Photo(CC)-BY Phillip Williams http://www.geograph.org.uk/photo/1342357
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- 19. Image (cc) DarwinBell http://www.flickr.com/photos/darwinbell/296553221/ “The predictive model was used as a trigger for intervention emails to the student.” 19
- 20. Image (cc) DarwinBell http://www.flickr.com/photos/darwinbell/296553221/ From: DONOTREPLY@mail.example.com You are in trouble. The computer predictive model gives you a 87.4322% chance of failing this course. You must see a tutor immediately. 20
- 21. Image (cc) DarwinBell http://www.flickr.com/photos/darwinbell/296553221/ From: DONOTREPLY@mail.example.com You are in trouble. The computer predictive model gives you a 87.4322% chance of failing this course. You must see a tutor immediately. 21 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.
- 22. Glasswinged butterfly, ?Greta oro Photo (CC)-BY-NC-ND by Greg Foster on Flickr http://www.flickr.com/photos/gregfoster/3365801458/ Principles • Privacy • Data protection • Ethics • Transparency
- 23. • LAK conferences •LASI workshops • Flare local meetings • Storm PhD training • Journal of LA • … and more! www.solaresearch.org
- 24. International Educational Data Mining Society 24 •Annual conference IEDMS • Journal of EDM • www.educationaldatamining.org
- 25. www.laceproject.eu Learning Analytics CommunityExchange (FP7) • Coordination and Support • Evidence Hub • Events: SoLAR Flare, 24 Oct 14, UK • Publications, briefings, webinars
- 26. at the OpenUniversity
- 27. The Open University •Largest university in the UK • 200,000 students • > 5,000 tutors • > 1,000 academic staff • Supported open learning • OpenLearn, YouTube, iTunesU, FutureLearn Milton Keynes
- 28. At scale, eachyear ~400 courses 200,000 students > 1 million assignments > 1 billion views of OU/BBC coproductions > 3 million Moodle transactions per day Photo (cc) Marieke IJsendoorn-Kuijpers http://www.flickr.com/photos/mape_s/333862026//
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- 31. Analytics Project Intervention andEvaluation Data Visualisations Ethics Framework Predictive Modelling Learning Experience Data Professional Development Small Data Student Tools
- 32. Photo (cc) jeroenbennink http://www.flickr.com/photos/jeroenbennink/2355768494/ To reduce dropout: • What will we do? • What change should we see? • Can we see that before the end of the course • e.g. VLE data, assessment data • Can we test whether it works? Intervention & Evaluation Framework
- 33. • Data Protection •Privacy • Transparency (related to Subject Access requests) • Whether students should be able to opt in/out • De-identification of data • Timeliness and Duty of Care (keeping data up to date) • Access to data (who should have access to the data, etc.) • Students abusing the system by misinformation Ethics • The use of student data outside OU systems (Facebook, Twitter, etc.) • Analysis of the data and the methods used (what assumptions are used to create the algorithm for the predictive model, should there be an independent audit?) • Purpose of applying a learning analytics approach • Profiling of students • How will it be done? • What do we tell students? • Should we tell students? – Students may feel ‘at-risk’/labelled Glasswinged butterfly, ? Greta oro cc licensed ( BY NC ND ) flickr photo by Greg Foster: http://www.flickr.com/photos/gregfoster/3365801458/
- 34. cc licensed (BY) flickr photo by Karen Roe: http://www.flickr.com/photos/karen_roe/4916422687/ Predictive modelling • To alert tutors • Financial planning • Quality assurance – Was retention better than predicted?
- 35. • Select students –Demographics, module data, VLE data (inc. key activity) • Trigger interventions – Emails, notes to tutor, etc Student Support Tool Data Interpreter in each Faculty cc licensed ( BY) flickr photo by Randy Robertson: http://www.flickr.com/photos/randysonofrobert/337922766/
- 36. Photo (CC)-BY-NC-SA MoonLee on Flickr https://www.flickr.com/photos/imagezen/65199660 Self-service data reports 36
- 37. Data Wranglers human sense-makers PhotoCC (BY-NC) Alan English CPA: https://www.flickr.com/photos/alanenglish/4198114139
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- 43. Context • National Curriculum •National testing (SATs, ages 7, 11, 14) • League tables • Analytics for tracking and monitoring 43 Photo (CC)-BY Thomas Galvez on Flickr https://www.flickr.com/photos/togawanderings/14212266277
- 44. School dashboards (GoogleImages) • Maybe chop the first slide about this. 44
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- 46.
- 47.
- 48. Dispositions analytics • Learningdispositions – Resilence • ‘Learning power’: Effective Lifelong Learning Inventory • “A framework for a coaching conversation which moves between identity and performance” • Schools to Graduate schools 48 Buckingham Shum and Deakin Crick, 2012 (LAK12)
- 49. ELLI Teacher view 49 Buckingham Shumand Deakin Crick, 2012 (LAK12)
- 50.
- 51. Enquiry Blogger teacherdashboard 51
- 52.
- 53. catnip for senior managers Photo (CC)-BYDylan Ashe https://www.flickr.com/photos/ackook/3929957511/
- 54. “When you canmeasure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science” – William Thomson, Lord Kelvin 54
- 55. Not everything thatcan be counted counts. Not everything that counts can be counted. – William Bruce Cameron 55 Photo (CC)-BY Paul Stainthorp https://www.flickr.com/photos/pstainthorp/5497004025 Not everything that can be counted counts. Not everything that counts can be counted. – William Bruce Cameron
- 56. Photo (CC)-BY kaybee07on Flickr https://www.flickr.com/photos/kurtbudiarto/7026555821
- 57. Thanks to: People: • OULearning Analytics: IET Student Statistics and Survey Team, Gill Kirkup and the other Data Wranglers, Kevin Mayles, Belinda Tynan, Simon Buckingham Shum, Rebecca Ferguson, Bart Rientes, Sharon Slade, Kelly Bevis, many others • LACE: Rebecca Ferguson, Simon Cross, Michelle Bailey, Rebecca Wilson, Evaghn De Souza, Natalie Eggleston, Oliver Millard, Gary Elliot-Citigottis, and our project partners. • The learning analytics community, including SoLAR, IEDMS, those I’ve met at LAK and LASI Funders: • LACE: European Commission 619424-FP7-ICT-2013-11
- 58. What Did YouLearn Today? What did you learn today? Feel free to share with others (on Twitter if you have access – use #laceproject) 58
- 59. “Learning Analytics: AGeneral Introduction and Perspectives from the UK” by Doug Clow, Institute of Educational Technology, The Open University, was presented at Skolverket, Stockholm on 9 October 2014. @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 59
- 60. cc licensed (BY ) flickr photo by David Goehring: http://flickr.com/photos/carbonnyc/33413040/
- 61. Data Wrangling 61 users data usersdata users data
- 62. What data dowe have about learners? • Demographics – Gender, age, ethnicity, socio-economic status, address • Previous educational experience – Schools, grades, results • Grades, scores, achievements, struggles • Attendance, location – Smart cards, proximity detectors • Online tracking – VLE / LMS data: views, posts, interactions, quiz results • Other online activity – Cross-tracking cookies • … more every week. 62
- 63. What can wedo with that data? • Identify students who need help – Simple or predictive • Trigger interventions – Via tutor, or direct • See which interventions work • Suggest resources or source of help – Learners like you found this helpful – This person might be able to help you 63
- 64. Not everything thatcan be counted counts. Not everything that counts can be counted. – William Bruce Cameron 64 Photo (C) The Office of His Holiness the Dalai Lama
Editor's Notes
- #2 Sorry I can’t present in Swedish. Or Norwegian or Danish. I do know one word of Scandinavian more than Google Translate! Læringsanalyse.
- #3 Sorry I can’t present in Swedish. Or Norwegian or Danish. I do know one word of Scandinavian more than Google Translate! Læringsanalyse.
- #4 Please copy, adapt, photograph, video. Tell your friends!
- #5 We’re using Lanyrd on the LACE project to connect people around events, To link people together, and the slides.
- #8 What if you could find that out? Data! Learning analytics is new technologies, but it is not a new idea.
- #9 Already seen this from Peter! Data mining, academic analytics, learner analytics – focus here is on the learning, not the management and administration of learning
- #10 Photo: Cloud Chamber at the German Electron Synchrotron DESY
- #11 Our data isn’t big. Most fits in Excel! Small data = Excel, Medium = laptop with R or other stats, Big = need special servers/cloud services
- #12 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.
- #13 Speed, scale, quality of response Get it to the learners and teachers
- #14 Morton has already mentioned this Concrete example Clever bits: alerts via tutor email (human connection), connect to existing support systems
- #17 M4 is a motorway that goes from London, England, all the way to South West Wales. Some people think the situaiton is complicated, but it’s really very simple. … it means the roads in England and Wales are managed by a different organisation.
- #18 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?
- #19 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.
- #20 “the predictive model was used as a trigger for intervention emails to the student”
- #21 “the predictive model was used as a trigger for intervention emails to the student”
- #22 “the predictive model was used as a trigger for intervention emails to the student”
- #23 Privacy – education makes space to fail, make mistakes, and learn from them – and not have that held against you. Data protection – longstanding EU legislation Ethics vast, complex, tricky. 70% chance to complete (about right) but 1% chance succeed. Alex the student. Vs ignoring info could help success. When does keeping the door to success open become giving unrealistic hope? Ethics critical path. Transparency – to learners, but to the outside. Shared processes.
- #26 Again heard from Peter.
- #29 Transactions figure from 2011! Second-largest Moodle installation in the world. We are big, but we care.
- #30 Better than Horizon Report or Gartner or your money back. 2014 report will be out very soon! Learning analytics is picked out as important.
- #31 Great vision. Belinda Tynan, also Simon Buckingham Shum, now in Australia. So bought in they made a nice video about it to sell it internally.
- #32 35 projects. Big investment. More besides.
- #33 Gives us: improved retention! Also database of action taken and effect – proximal behavioural change, and final retention data. Pilot on 15 target modules now. All modules from March/April?
- #34 Sharon Slade
- #35 Ethics: 1% chance of success – right there!
- #36 Part of change to how we support students.
- #38 Professional development, capacity building. Named individuals. Work with the data, mediate it for the faculty. Two-way, feeding back to the data capture, presentation teams. Find the stuff that’s valuable, and automate that, dashboard it.
- #39 What they ideally do, not what they actually do
- #42 And now, a commercial break. We’re also interested in analytics on MOOCs. FutureLearn new MOOC platform with a British accent. Now has real students on it, successful courses starting up for second time.
- #45 Massive investment by educational software vendors, from VC-backed startups to large international media companies. LMS vendors all have an analytics product. Mostly American, but many British too, and UK schools use them a lot.
- #46 I picked this one at random because I saw a presentation at London Knowledge Lab.
- #50 Cohort dispositional analytics. Building critical self-awareness. Correlations with success measures, but complex relationship. Learning power goes down over time in school!
- #54 Senior managers care about bums on seats and money. They’re paid to. The good ones – most of them – also care about other things. You care about teaching and learning. Get involved!
- #55 Two contrasting visions.
- #56 We are working with numbers, but the numbers are people. Remember that compassionate, human connection.
- #57 There’s a lot that looks impressive, but less elegant beneath the surface. Swan metaphor Learning analytics is a new field. You can move forward! Don’t be overawed. Make things better for your learners.
- #58 Big thanks in small fonts
- #59 In order to gather feedback on the talk you may wish to invite people to share their thoughts. Please note that if comments are not provided in a digital form (e.g. using Twitter) it will be difficult to capture and reuse the comments in tools such as Storify. You should not use this slide if you have not encouraged use of Twitter.
- #60 )
- #62 Capacity building
- #64 To help the learner! Not just tracking.