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Building Analytics Capability @open.edu Building Analytics Capability @open.edu Presentation Transcript

  • JISC CETIS 2013 Conference: Analytics and Institutional CapabilitiesBuilding AnalyticsCapability @open.eduSimon Buckingham ShumProfessor of Learning InformaticsKnowledge Media Institute, The Open University, UKhttp://simon.buckinghamshum.net@sbskmi
  • bit.ly/OULAprof  
  • Same  outcomes,     but  higher  scores?     Learning  Analy=cs  as     Evolu&onary  Technology.  Same  training  +  educa=onal  paradigms     •  more  engaging   •  beBer  assessed   •  beBer  outcomes  3   •  deliverable  at  scale  
  • Learning  dynamics  we    couldn’t  assess  before?     Learning  Analy=cs  as     Revolu&onary  Technology.   A  vehicle  for  paradigm  shiF?     •  interpersonal  learning  networks   •  quality  of  discourse  +  wri=ng     •  lifelong  learning  disposi=ons   •  problem  solving  strategies   •  lifewide  learning  
  • open.eduBI perspective 5
  • OU  data  warehouse  (in  progress)   IT corral keyinstitutional data in the IT provide data central warehouse 1 2 dictionary IT provide dataBusiness datausers propose 5 Data   3 marts and cubes action Warehouse   for commonly used data sets “Data Wranglers” assist staff in understanding BI 4 OU Analytics Board Explore the challenge/issue/problem/ opportunity/question using SAS/preferred tool
  • open.edu VLEperspective 7
  • VLE  Analy;cs  @  the  OU   Virtual   Learning   Environment   Data   Warehouse   Usage  sta;s;cs  at  system,  faculty  and   ‘Par;cipa;on  Tracking’  func;on  to  track   module  level  –  general  paCerns   individual  students’  interac;on  with  specific   online  learning  ac;vi;es   In  pilot  2012/13  
  • VLE  Analy;cs  @  the  OU   Virtual   Learning   Environment   Data   Warehouse   Usage  sta;s;cs  at  system,  faculty  and   ‘Par;cipa;on  Tracking’  func;on  to  track   module  level  –  general  paCerns   individual  students’  interac;on  with  specific   online  learning  ac;vi;es   In  pilot  2012/13  
  • VLE  Analy;cs  @  the  OU   Virtual   Learning   Environment   Data   Warehouse   Usage  sta;s;cs  at  system,  faculty  and   ‘Par;cipa;on  Tracking’  func;on  to  track   module  level  –  general  paCerns   individual  students’  interac;on  with  specific   online  learning  ac;vi;es   In  pilot  2012/13  
  • open.edupredictivemodelling 11
  • Predictive analytics Demo-­‐ VLE   graphics   interac=on   ? Registra=on   Library   PaBern   interac=on   How early can we predict likelihood of dropout, formal CRM   OpenLearn   withdrawal, failure? contact   interac=on   Now exploring conventional Assignment   Futurelearn   statistics, machine learning grades   interac=on   and growing datasets OU  track   Social  App  X   New fees regime may well record   interac=on   change student behaviour…
  • OU Analytics: Predictive modelling§  Probability models help us to identify patterns of success that vary between: §  student groups / areas of curriculum / study methods Best predictors of§  Benefits future success: previous OU study §  provide a more robust comparison of data – quantity module pass rates and results §  support the institution in identifying aspects of good performance that can be shared, and aspects where improvement could be realisedOU Student Statistics & Surveys Team, Institute of Educational Technology 13
  • Improving student retention with predictive analytics 4 predictive models: final result (pass/fail) Demo- final numerical score graphics drop in the next TMA score of the next TMA Previous results VLE activityA.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
  • open.edu Libraryperspective 15
  • Learning Analytics – the Library dimension Student achievement Recommender services Library use ‘Students who looked at this article also looked at this article’ ‘Students on your course are looking at these articles’ Library Impact Data Project – Huddersfield Universityhttp://www.flickr.com/photos/davepattern/6928727645/sizes/o/in/photostream/
  • open.edu Researchperspective 17
  • Visualizing  and  filtering  social  ;es  in   SocialLearn  by  topic  and  type  Schreurs,  B.,  Teplovs,  C.,  Ferguson,  R.,  De  Laat,  M.  and  Buckingham  Shum,  S.,  Visualizing  Social  Learning  Ties  by  Type  and  Topic:  Ra;onale  and  Concept  Demonstrator.  In:  Proc.  3rd  Interna6onal  Conference  on  Learning  Analy6cs  &  Knowledge  (Leuven,  BE,  8-­‐12  April,  2013).  ACM  hCps://dl.dropbox.com/u/15264330/papers/Schreurs-­‐etal-­‐LAK2013.pdf  
  • Discourse analytics on webinar textchat Given a 2.5 hour webinar, where in the live textchat were the most effective learning conversations? Sheffield, UK not as sunny as yesterday - still Not at the start and end of a webinar, but See you! warm Greetings from Hong if we zoom in on a peak… bye for now! Kong bye, and thank you 80 Morning from Wiltshire, Bye all for now sunny here! 60 40 20 0 9:28 9:32 10:13 11:48 12:00 12:05 12:04 9:36 9:40 9:41 9:46 9:50 9:53 9:56 10:00 10:05 10:07 10:07 10:09 10:17 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 11:52 11:54 12:03 -20 -40 Average Exploratory -60Ferguson, 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
  • Discourse analytics on webinar textchat Given a 2.5 hour webinar, where in the live textchat were the most effective learning Classified conversations? as “exploratory talk” 100 (more substantive 50 for learning) 0 9:28 “non- 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 -50 exploratory Averag ” -100Ferguson, 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
  • 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
  • Analytics for “21st Century Competencies & Learning Dispositions” Different social Questioning and network patterns in challenging may load different contexts onto Critical Curiosity may load onto Learning Relationships Repeated attempts to pass an online test Sharing relevant may load onto resources from other Resilience contexts may load onto Meaning MakingBuckingham Shum, S. and Deakin Crick, R. (2012). Learning Dispositions and Transferable Competencies: Pedagogy, Modelling andLearning Analytics. Proc. 2nd Int. Conf. Learning Analytics & Knowledge. (29 Apr-2 May, Vancouver). Eprint: http://oro.open.ac.uk/32823
  • open.educomingsoon… 23
  • On the horizon… MOOCs + Analytics… Educ Research at SCALE Partnerships/ Collab What Research Data? Biz Models ‘vs’ Open Ethicshttp://people.kmi.open.ac.uk/sbs/2013/01/emerging-mooc-data-analytics-ecosystem
  • On the horizon…Educational Data Scientists