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insight-centre-galway-learning-analytics

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Invited talk, INSIGHT Centre for Data Analytics, Univ. Galway, 2 Oct 2013, http://www.insight-centre.org …

Invited talk, INSIGHT Centre for Data Analytics, Univ. Galway, 2 Oct 2013, http://www.insight-centre.org

Abstract:

Data and analytics are transforming how organisations work in all sectors. While there are clearly ethical issues around big data and privacy, there may also be an argument that educational institutions have a moral obligation to use all the information they have to maximize the learner's progress. So, assuming education can't (arguably shouldn't) resist this revolution, the question is how to harness this new capability intelligently. Learning Analytics is an exploding research field and startup market: do leaders know what to ask when the vendors roll up with dazzling dashboards? In this talk I'll provide an overview of developments, and consider some of the key questions we should be asking. Like any modelling technology and accounting system, analytics are not neutral, and do not passively describe sociotechnical reality: they begin to shape it. Moreover, they start with the things that are easiest to count, which doesn't necessarily equate to the things we value in learning. Given the crisis in education at many levels, what realities do we want analytics to perpetuate, or bring into being?

Bio:

Simon Buckingham Shum is Professor of Learning Informatics at the UK Open University's Knowledge Media Institute. He researches, teaches and consults on Learning Analytics, Collective Intelligence and Argument Visualization. His background is B.Sc. Psychology, M.Sc. Ergonomics and Ph.D. Human-Computer Interaction. He co-edited Visualizing Argumentation (Springer 2003), the standard reference in the field, followed by Knowledge Cartography (2008). In the field of Learning Analytics, he served as Program Co-Chair of the 2nd International Learning Analytics LAK12 conference, chaired the LAK13 Discourse-Centric Learning Analytics workshop, and the LASI13 Dispositional Learning Analytics workshop. He is a co-founder of the Society for Learning Analytics Research, Compendium Institute, LearningEmergence.net, and was Co-Founder and General Editor of the Journal of Interactive Media in Education. He serves on the Advisory Groups for a variety of learning analytics initiatives in education and enterprise, and is a Visiting Fellow at University of Bristol Graduate School of Education. Contact him via http://simon.buckinghamshum.net

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  • 1. Learning Analytics The New Burden of Knowledge Simon Buckingham Shum Knowledge Media Institute The Open University UK http://simon.buckinghamshum.net http://linkedin.com/in/simon INSIGHT Centre for Data Analytics, Univ. Galway, 2 Oct 2013 http://www.insight-centre.org @sbskmi #LearningAnalytics
  • 2. mission walk out with better questions than you can ask right now about analytics new tech and collaboration opportunities to advance education 2
  • 3. why are we seeing this?... 3
  • 4. Why are we seeing this?... 4 VLEs + Analytics Publishers + Analytics
  • 5. 5 Audrey Waters: http://hackeducation.com/2012/11/19/top-ed-tech-trends-of-2012-the-business-of-ed-tech Ed-Tech startups explosive growth Why are we seeing this?...
  • 6. 6https://www.edx.org/about “this is big data, giving us the chance to ask big questions about learning” Why are we seeing this?...
  • 7. 7http://careers.stackoverflow.com/jobs/35348/software-engineer-analytics-coursera Why are we seeing this?...
  • 8. 8 Why are we seeing this?... http://www.independent.ie/lifestyle/education/trinity-joins-elite-colleges-to-offer-free-online-courses-29355438.html
  • 9. the data/analytics tsunami is about to hit the education sector 9
  • 10. Data and analytics are transforming business, government and public services 10 Why would Higher Education be immune? Why wouldn’t a sector focused on evidence-based thinking and action welcome it? A critical discussion is emerging More later…
  • 11. 11L. Johnson, R. Smith, H. Willis, A. Levine, and K. Haywood, The 2011 Horizon Report (Austin, TX: The New Media Consortium, 2011), http://www.nmc.org/pdf/2011-Horizon-Report.pdf NMC Horizon 2011 Report: Learning Analytics (4-5yrs adoption) Analytics is being heralded… (2013 report)
  • 12. 12 Continuous coverage… http://www.online-educa.com/OEB_Newsportal/whats-so-big-about-big-data
  • 13. 13 …and debated… http://www.online-educa.com/OEB_Newsportal/we-urgently-need-to-safeguard-free-will-in-the-age-of-big-data
  • 14. 14
  • 15. 15
  • 16. Tectonic forces are reshaping the learning landscape… 16
  • 17. the opportunity for learning design learning sciences 17
  • 18. From an analytics product review… 18
  • 19. From an analytics product review… “Some have tried to argue that this technology doesn't work out cost effectively when compared to conventional tests... but this misses a huge point. More often than not, we test after the event and discover the problem — but this is too late..” 19
  • 20. Aquarium Analytics! 20
  • 21. 21
  • 22. How is your aquatic ecosystem? “This means that the keeper can be notified before water conditions directly harm the fish—an assured outcome of predictive software that lets you know if it looks like the pH is due to drop, or the temperature is on its way up. This way, it’s a real fish saver, as opposed to a forensic examiner, post-wipeout.” (From a review of Seneye, in a hobbyist magazine) 22
  • 23. How is your learning ecosystem? This means that the teacher can be notified before learning conditions directly harm the students — an assured outcome of predictive software that lets you know if it looks like engagement is due to drop, or distraction is on its way up. This way, it’s a real student saver, as opposed to a forensic examiner, post-wipeout. 23
  • 24. 24 Back to Aquarium Analytics…
  • 25. 25 fish aquarium science learners? learning science instructional design Back to Aquarium Analytics…
  • 26. Purdue University Signals: real time traffic- lights for students based on predictive model 26
  • 27. Purdue University Signals: real time traffic- lights for students based on predictive model 27 Predicted 66%-80% of struggling students who needed help MODEL: •  ACT or SAT score •  Overall grade-point average •  CMS usage composite •  CMS assessment composite •  CMS assignment composite •  CMS calendar composite Campbell et al (2007). Academic Analytics: A New Tool for a New Era, EDUCAUSE Review, vol. 42, no. 4 (July/August 2007): 40– 57. http://bit.ly/lmxG2x
  • 28. Purdue University Signals: real time traffic- lights for students based on predictive model 28 “Results thus far show that students who have engaged with Course Signals have higher average grades and seek out help resources at a higher rate than other students.” Pistilli, M. D., Arnold, K. and Bethune, M., Signals: Using Academic Analytics to Promote Student Success. EDUCAUSE Review Online, July/Aug., (2012). http://www.educause.edu/ero/article/signals-using-academic- analytics-promote-student-success
  • 29. Predictive analytics @open.edu Registra)on   Pa.ern   CRM   contact   VLE   interac)on   Grades   Demo-­‐ graphics   ? How early can we predict likelihood of dropout, formal withdrawal, failure? Now exploring conventional statistics, machine learning and growing datasets Library   interac)on   OpenLearn   interac)on   FutureLearn   interac)on   Social  App  X   interac)on   OU  history  
  • 30. Predictive analytics @open.edu A.L. Wolff and Z. Zdrahal (2012). Improving Retention by Identifying and Supporting “At-risk” Students. EDUCAUSE Review Online, July- August 2012. http://www.educause.edu/ero/article/improving-retention-identifying-and-supporting-risk-students Test a range of predictive models: final result (pass/fail) final numerical score drop in the next TMA score of the next TMA Demo- graphics Previous results VLE activity Adding in user interaction data from the VLE
  • 31. the opportunity for the learning sciences to combine with your university’s collective intelligence 31
  • 32. macro meso micro analytics 32
  • 33. Macro/Meso/Micro Learning Analytics Macro: region/state/national/international League Tables Data Interoperability Initiatives
  • 34. Macro/Meso/Micro Learning Analytics Meso: institution-wide Macro: region/state/national/international Business Intelligence Products
  • 35. Business Intelligence ≠ Learning Analytics
  • 36. Micro: individual user actions (and hence cohort) Macro/Meso/Micro Learning Analytics Meso: institution-wide Macro: region/state/national/international Learning Analytics
  • 37. Micro: individual user actions (and hence cohort) Hard distinctions between Learning + Academic analytics may dissolve Meso: institution-wide Macro: region/state/national/international Aggregation of user traces enriches meso + macro analytics with finer-grained process data …as they get joined up, each level enriches the others
  • 38. Micro: individual user actions (and hence cohort) Hard distinctions between Learning + Academic analytics may dissolve Meso: institution-wide Macro: region/state/national/international Aggregation of user traces enriches meso + macro analytics with finer-grained process data Breadth + depth from macro + meso levels add power to micro analytics …as they get joined up, each level enriches the others
  • 39. Insight Centre intersection with learning analytics? 39
  • 40. Insight Centre R&D 40http://www.insight-centre.org There is active research (and often product development) at the intersection of education and all of these tech R&D challenges
  • 41. Insight Centre R&D could tackle education 41 http://adenu.ia.uned.es/workshops/recsystel2010 www.educationaldatamining.org http://linkedup-project.eu/2013/03/17/using-linked-data-in-learning-analytics-a-tutorial-by-the-linkedup-consortium http://www.slideshare.net/erik.duval/20130703-lasi-stanforderik http://www.dia.uniroma3.it/~umap2013/
  • 42. predictive models are exciting but there are many other kinds of analytics 42
  • 43. Analytics coming to a VLE near you: e.g. Blackboard 43 http://www.blackboard.com/platforms/analytics/overview.aspx http://www.blackboard.com/Platforms/Analytics/Products/Blackboard-Analytics-for-Learn.aspx
  • 44. 44 Student Activity Dashboard (Erik Duval) Duval E. (2011) Attention please!: learning analytics for visualization and recommendation. Proceedings of the 1st International Conference on Learning Analytics and Knowledge. Banff, Alberta, Canada: ACM, 9-17.
  • 45. 45 Khan Academy http://www.youtube.com/watch?v=DLt6mMQH1OY Khan Academy has extended great instructional movies with a tutoring platform with detailed analytics
  • 46. 46 https://grockit.com/research Adaptive platforms generate fine-grained analytics on curriculum mastery
  • 47. Intelligent tutoring for skills mastery (CMU) http://oli.cmu.edu Lovett M, Meyer O and Thille C. (2008) The Open Learning Initiative: Measuring the effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media in Education 14. http://jime.open.ac.uk/article/2008-14/352 “In this study, results showed that OLI-Statistics students [blended learning] learned a full semester’s worth of material in half as much time and performed as well or better than students learning from traditional instruction over a full semester.”
  • 48. 48 Are students using the right tools at the right time in the right way? (Abelardo Pardo, LAK13 Keynote) http://www.slideshare.net/abelardo_pardo/bridging-the-middle-space-with-learning-analytics
  • 49. Social Learning Analytics Buckingham Shum, Sand Ferguson, R (2012). Social Learning Analytics. Journal of Educational Technology and Society, 15(3) pp. 3–26. http://oro.open.ac.uk/34092 •  Explosive growth in social media •  The open/free content paradigm •  Evidence of a global shift in societal attitudes which increasingly values participation •  Innovation depends on reciprocal social relationships, tacit knowing
  • 50. Social Network Analysis (SNAPP) 50Bakharia, A. and Dawson, S., SNAPP: a bird's-eye view of temporal participant interaction. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge (Banff, Alberta, Canada, 2011). ACM. pp.168-173 What’s going on in these discussion forums?
  • 51. Social Network Analysis (SNAPP) 51 http://www.slideshare.net/aneeshabakharia/snapp-20minute-presentation
  • 52. Social Network Analysis (SNAPP) 52 http://www.slideshare.net/aneeshabakharia/snapp-20minute-presentation 2 learners connect otherwise separate clusters tutor only engaging with active students, ignoring disengaged ones on the edge
  • 53. Social Learning Analytics about to appear in products… 53 http://www.desire2learn.com/products/analytics (this is from a beta demo)
  • 54. Semantic Social Network Analytics De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. Discourse-centric learning analytics. 1st International Conference on Learning Analytics & Knowledge (Banff, 27 Mar-1 Apr, 2011) http://oro.open.ac.uk/25829
  • 55. Visualizing and filtering social ties in SocialLearn by topic and type Schreurs B, Teplovs C, Ferguson R, De Laat M and Buckingham Shum S. (2013) Visualizing Social Learning Ties by Type and Topic: Rationale and Concept Demonstrator. Proc. 3rd International Conference on Learning Analytics & Knowledge. Leuven, BE: ACM, 33-37. Open Access Eprint: http://oro.open.ac.uk/36891
  • 56. Visualizing and filtering social ties in SocialLearn by topic and type
  • 57. Visualizing and filtering social ties in SocialLearn by topic and type
  • 58. Visualizing and filtering social ties in SocialLearn by topic and type
  • 59. Visualizing and filtering social ties in SocialLearn by topic and type
  • 60. discourse analytics are students using language as a knowledge-building tool? 60
  • 61. The promise of language technologies Beyond number / size / frequency of posts or ‘trending topic’ ? http://www.glennsasscer.com/wordpress/wp-content/uploads/2011/10/iceberg.jpg
  • 62. Discourse analytics on webinar textchat Ferguson, R. and Buckingham Shum, S., Learning analytics to identify exploratory dialogue within synchronous text chat. In: 1st International Conference on Learning Analytics and Knowledge (Banff, Canada, 2011). ACM Can we spot the quality learning conversations in a 2.5 hr webinar?
  • 63. -60 -40 -20 0 20 40 60 80 9:28 9:32 9:36 9:40 9:41 9:46 9:50 9:53 9:56 10:00 10:05 10:07 10:07 10:09 10:13 10:17 10:23 10:27 10:31 10:35 10:40 10:45 10:52 10:55 11:04 11:08 11:11 11:17 11:20 11:24 11:26 11:28 11:31 11:32 11:35 11:36 11:38 11:39 11:41 11:44 11:46 11:48 11:52 11:54 12:00 12:03 12:04 12:05 Average Exploratory Discourse analytics on webinar textchat Sheffield, UK not as sunny as yesterday - still warm Greetings from Hong Kong Morning from Wiltshire, sunny here! See you! bye for now! bye, and thank you Bye all for now Given a 2.5 hour webinar, where in the live textchat were the most effective learning conversations? Not at the start and end of a webinar… Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S., An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. In: Proc. 3rd International Conference on Learning Analytics & Knowledge (Leuven, BE, 8-12 April, 2013). ACM. http://oro.open.ac.uk/36664
  • 64. -60 -40 -20 0 20 40 60 80 9:28 9:32 9:36 9:40 9:41 9:46 9:50 9:53 9:56 10:00 10:05 10:07 10:07 10:09 10:13 10:17 10:23 10:27 10:31 10:35 10:40 10:45 10:52 10:55 11:04 11:08 11:11 11:17 11:20 11:24 11:26 11:28 11:31 11:32 11:35 11:36 11:38 11:39 11:41 11:44 11:46 11:48 11:52 11:54 12:00 12:03 12:04 12:05 Average Exploratory Discourse analytics on webinar textchat Given a 2.5 hour webinar, where in the live textchat were the most effective learning conversations? Not at the start and end of a webinar but if we zoom in on a peak… Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S., An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. In: Proc. 3rd International Conference on Learning Analytics & Knowledge (Leuven, BE, 8-12 April, 2013). ACM. http://oro.open.ac.uk/36664
  • 65. Discourse analytics on webinar textchat -100 -50 0 50 100 9:28 9:40 9:50 10:00 10:07 10:17 10:31 10:45 11:04 11:17 11:26 11:32 11:38 11:44 11:52 12:03 Averag Classified as “exploratory talk” (more substantive for learning) “non- exploratory” Given a 2.5 hour webinar, where in the live textchat were the most effective learning conversations? Not at the start and end of a webinar but if we zoom in on a peak… Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S., An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. In: Proc. 3rd International Conference on Learning Analytics & Knowledge (Leuven, BE, 8-12 April, 2013). ACM. http://oro.open.ac.uk/36664
  • 66. Discourse analytics on webinar textchat Visualizing by individual user. The gradient of the threshold line is adjusted to every 5 posts in 6 classified as “Exploratory Talk” Ferguson, R., Wei, Z., He, Y. and Buckingham Shum, S., An Evaluation of Learning Analytics to Identify Exploratory Dialogue in Online Discussions. In: Proc. 3rd International Conference on Learning Analytics & Knowledge (Leuven, BE, 8-12 April, 2013). ACM. http://oro.open.ac.uk/36664
  • 67. “Rhetorical parsing” to identify constructions signifying scholarly writing OPEN QUESTION: “… little is known …” “… role … has been elusive” “Current data is insufficient …” CONTRASTING IDEAS: “… unorthodox view resolves …” “In contrast with previous hypotheses ...” “... inconsistent with past findings ...” SURPRISE: “We have recently observed ... surprisingly” “We have identified ... unusual” “The recent discovery ... suggests intriguing roles” http://technologies.kmi.open.ac.uk/cohere/2012/01/09/cohere-plus-automated-rhetorical-annotation De Liddo, A., Sándor, Á. and Buckingham Shum, S., Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine Annotation Study. Computer Supported Cooperative Work, 21, 4-5, (2012), 417-448. http://oro.open.ac.uk/31052
  • 68. 68 Xerox Incremental Parser (XIP) Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search. Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.
  • 69. 69 Xerox Incremental Parser (XIP) Sándor, Á. and Vorndran, A. (2010). The detection of salient messages from social science research papers and its application in document search. Workshop on Natural Language Processing Tools Applied to Discourse Analysis in Psychology, Buenos Aires, Argentina, May 10-14. 2010.
  • 70. Initial evaluation of XIP is promising, but methodologically complex Human analyst XIP A striking example – but not all were like this (De Liddo et al, 2012) 19 sentences annotated 22 sentences annotated 11 sentences same as human annotation 71 sentences annotated 59 sentences annotated 42 sentences same as human annotation Document 1 Document 2 Extract from annotation comparison:
  • 71. Xerox Incremental Parser (XIP) XIP’s raw output is fine for NLP machines/researchers, but not learner/educator friendly
  • 72. Xerox Incremental Parser (XIP) 5000 (or even 30) plain text files… we need overviews of XIP analyses from a corpus
  • 73. Making XIP analytics visible: Annotations on the full text using the OU’s Cohere social sensemaking app (Firefox add-on)
  • 74. XIP Dashboard All papers by year and concept, with colour = concept density (v2 mockup) 74 Simsek D, Buckingham Shum S, Sándor Á, De Liddo A and Ferguson R. (2013) XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of Scientific Metadiscourse. 1st International Workshop on Discourse-Centric Learning Analytics, at 3rd International Conference on Learning Analytics & Knowledge. Leuven, BE (Apr. 8-12, 2013). Open Access Eprint: http://oro.open.ac.uk/37391
  • 75. intrinsic motivation self-regulation resilience 75
  • 76. Why do dispositions matter? 76 “Knowledge of methods alone will not suffice: there must be the desire, the will, to employ them. This desire is an affair of personal disposition.” John Dewey Dewey, J. How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process. Heath and Co, Boston, 1933
  • 77. “In the growth mindset, people believe that their talents and abilities can be developed through passion, education, and persistence … It’s about a commitment to … taking informed risks … surrounding yourself with people who will challenge you to grow” Carol Dweck 77 Interview with Carol Dweck: http://interviewscoertvisser.blogspot.co.uk/2007/11/interview-with-carol-dweck_4897.html Why do dispositions matter?
  • 78. “We’re looking at the profiles of what it means to be effective in the 21st century. […] Resilience will be the defining concept. When challenged and bent, you learn and bounce back stronger.” “Dispositions are now at least as important as Knowledge and Skills. …They cannot be taught. They can only be cultivated.” John Seely Brown 78 http://reimaginingeducation.org conference (May 28, 2013) Dispositions clip: http://www.c-spanvideo.org/clip/4457327 Whole talk: http://www.c-spanvideo.org/program/SecD Why do dispositions matter?
  • 79. How can we model and quantify learning dispositions in order to develop analytics? 79
  • 80. Validated as loading onto 7 dimensions of “Learning Power” Changing & Learning Meaning Making Critical Curiosity Creativity Learning Relationships Strategic Awareness Resilience Being Stuck & Static Data Accumulation Passivity Being Rule Bound Isolation & Dependence Being Robotic Fragility & Dependence Ruth Deakin Crick Grad. School of Education
  • 81. Learning to Learn: 7 Dimensions of Learning Power Factor analysis of the literature plus expert interviews: identified seven dimensions of effective “learning power”, since validated empirically with learners at many levels. (Deakin Crick, Broadfoot and Claxton, 2004)
  • 82. Learning to Learn: 7 Dimensions of Learning Power 82
  • 83. platforms for Dispositional Learning Analytics 83DLA Workshop, Stanford (July 2013) http://learningemergence.net/events/lasi-dla-wkshp
  • 84. Analytics for lifelong/lifewide learning dispositions: ELLI Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, Vancouver). Eprint: http://oro.open.ac.uk/32823
  • 85. ELLI generates cohort data for each dimension Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, Vancouver). Eprint: http://oro.open.ac.uk/32823
  • 86. Primary School EnquiryBloggers Bushfield School, Wolverton, UK EnquiryBlogger: blogging for Learning Power & Authentic Enquiry http://learningemergence.net/2012/06/20/enquiryblogger-for-learning-power-authentic-enquiry
  • 87. Masters level EnquiryBloggers Graduate School of Education, University of Bristol EnquiryBlogger: blogging for Learning Power & Authentic Enquiry http://learningemergence.net/2012/06/20/enquiryblogger-for-learning-power-authentic-enquiry
  • 88. EnquiryBlogger dashboard – direct navigation to learner’s blogs from the visual analytic
  • 89. Could a platform generate an ELLI profile from user traces? Shaofu Huang: Prototyping Learning Power Modelling in SocialLearn http://www.open.ac.uk/blogs/SocialLearnResearch/2012/06/20/social-learning-analytics-symposium Different social network patterns in different contexts may load onto Learning Relationships Questioning and challenging may load onto Critical Curiosity Sharing relevant resources from other contexts may load onto Meaning Making Repeated attempts to pass an online test may load onto Resilience
  • 90. Your most recent mood comment: “Great, at last I have found all the resources that I have been looking for, thanks to! Steve and Ellen.! In your last discussion with your mentor, you decided to work on your resilience by taking on more learning challenges Your ELLI Spider shows that you have made a start on working on your resilience, and that you are also beginning to work on your creativity, which you identified as another area to work on. 1 2 3 45 Envisioning a social learning analytics dashboard Ferguson R and Buckingham Shum S. (2012) Social Learning Analytics: Five Approaches. Proc. 2nd International Conference on Learning Analytics & Knowledge. Vancouver, 29 Apr-2 May: ACM: New York, 23-33. DOI: http://dx.doi.org/10.1145/2330601.2330616 Eprint: http://oro.open.ac.uk/32910
  • 91. thorny issues 91
  • 92. Accounting tools are not neutral “accounting tools...do not simply aid the measurement of economic activity, they shape the reality they measure” Du Gay, P. and Pryke, M. (2002) Cultural Economy: Cultural Analysis and Commercial Life. Sage, London. pp. 12-13
  • 93. cf. Bowker and Starr’s “Sorting Things Out” on classification schemes Buckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling and Learning Analytics. Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, 2012, Vancouver, BC). ACM. Eprint: http://oro.open.ac.uk/32823 “A marker of the health of the learning analytics field will be the quality of debate around what the technology renders visible and leaves invisible.”
  • 94. The Wal-Martification of education? 94http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college http://lak12.wikispaces.com/Recordings “The basic question is not what can we measure? The basic question is what does a good education look like? Big questions. “data narrowness” “instrumental learning” “students with no curiosity”
  • 95. 95 “Our analytics are our pedagogy” (and epistemology) They promote assessment regimes — which drive (and strangle) educational innovation Knight S., Buckingham Shum S. and Littleton K. (2013) Epistemology, Pedagogy, Assessment and Learning Analytics. Proc. 3rd International Conference on Learning Analytics & Knowledge. Leuven, BE: ACM, 75-84 Open Access Eprint: http://oro.open.ac.uk/36635
  • 96. learning analytics are not neutral data does not “speak for itself” 96
  • 97. Analytics cycle (Doug Clow) h.p://www.slideshare.net/dougclow/the-­‐learning-­‐analy)cs-­‐cycle-­‐closing-­‐the-­‐loop-­‐effec)vely  (slide  5)   97
  • 98. Analytics cycle (George Siemens) h.p://www.slideshare.net/gsiemens/eli-­‐2012-­‐sensemaking-­‐analy)cs  (slide  7)   98
  • 99. All analytics are infused with human values Elaborated version of figure from Doug Clow: h.p://www.slideshare.net/dougclow/the-­‐learning-­‐analy)cs-­‐cycle-­‐closing-­‐the-­‐loop-­‐effec)vely  (slide  5) 99 What kinds of learners? What kinds of learning? What data could be generated digitally from the use context? (you can invent future technologies if need) Does your theory predict patterns signifying learning? What human +/or software interventions / recommendations? How to render the analytics, for whom, and will they understand them? What analytical tools could be used to find such patterns? ethics
  • 100. 100 http://www.flickr.com/photos/somegeekintn/3709203268/ Who gets to define, and hold, the magnifying glass?
  • 101. 101 http://www.flickr.com/photos/centralasian/6396004353/sizes/m/in/photostream/ Learning Analytics should provide mirrors for learners to become more reflective, and less dependent
  • 102. to go deeper… 102
  • 103. Join the community… 103 http://SoLAResearch.org http://LAKconference.org replays of all previous conference presentations
  • 104. Join the community… 104 http://www.solaresearch.org/events/lasi replays of all sessions
  • 105. Open course on systemic deployment of analytics 105https://www.canvas.net/courses/policy-and-strategy-for-systemic-deployment-of-learning-analytics Universities and companies exploring institutional strategy, policy and infrastructure
  • 106. JISC Briefings on Learning Analytics 106http://publications.cetis.ac.uk/c/analytics
  • 107. EDUCAUSE Briefings on Learning Analytics 107 http://www.educause.edu/library/learning-analytics
  • 108. Learning Analytics Policy Brief (UNESCO • IITE) 108http://bit.ly/LearningAnalytics
  • 109. http://LearningEmergence.net
  • 110. We all love a good big brother (don’t we?) “A responsible school/university in 2016 will use every form of data shared by students in order to maximise their success.” Discuss Thank you!

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