Analytics: Sensemaking, Performance, Prediction

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  • http://www.nifc.gov/safety/mann_gulch/suggested_reading/The_Collapse_of_Sensemaking_in_Organizations_The_Mann_Gulch.pdf
  • http://www.bruno-latour.fr/sites/default/files/P-129-THES-GB.pdf
  • A unified framework for multi-level analysis of distributed learninghttp://dl.acm.org/citation.cfm?id=2090124&CFID=82269174&CFTOKEN=35344405
  • Attention please!: learning analytics for visualization and recommendationhttp://dl.acm.org/citation.cfm?id=2090118&CFID=82269174&CFTOKEN=35344405
  • Learning networks, crowds and communitieshttp://dl.acm.org/citation.cfm?id=2090119&CFID=82269174&CFTOKEN=35344405
  • Discourse-centric learning analytics http://dl.acm.org/citation.cfm?id=2090120&CFID=82269174&CFTOKEN=35344405
  • Social Learning Analyticshttp://kmi.open.ac.uk/publications/pdf/kmi-11-01.pdf
  • iSpot analysed: participatory learning and reputation http://dl.acm.org/citation.cfm?id=2090121&CFID=82269174&CFTOKEN=35344405
  • Macfadyen, L.P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588-599.Campbell, J. P., Collins, W.B., Finnegan, C., & Gage, K. (2006). "Academic analytics: Using the CMS as an early warning system." WebCT Impact 2006. Chicago, IL
  • http://sloanreview.mit.edu/feature/achieving-competitive-advantage-through-analytics/
  • Analytics: Sensemaking, Performance, Prediction

    1. 1. Analytics:Sensemaking, Prediction & Performance George Siemens, PhD June 6, 2012 Presented to:8th Annual Innovations in e-Learning Symposium George Mason University
    2. 2. The contextWhy and what of analytics?ExamplesColliding ideasOrganizations and the analytics model
    3. 3. The contextWhy and what of analytics?ExamplesColliding ideasOrganizations and the analytics model
    4. 4. http://www.slideshare.net/gsiemens/
    5. 5. August 5, 1949: Mann Gulch
    6. 6. December 2–3, 1984: Bhopal
    7. 7. April 14, 1994: Black Hawk Incident
    8. 8. Add:terrorist attacks, Space Shuttle Columbia, 2008financial crisis, etc, etc.
    9. 9. Progressive reliance on sensemaking in systemsrather than as individuals
    10. 10. Sensemaking“Sensemaking is a motivated, continuous effortto understand connections . . . in order toanticipate their trajectories and act effectively” (Klein et al. 2006)
    11. 11. We can’t understand how people make sensewithout considering the system
    12. 12. 2008, financial Mann Gulch SS Columbia9/11 Bhopal 7/7 Mumbai
    13. 13. 2008, financial Mann Gulch SS Columbia 9/11 Bhopal 7/7Sensemaking, prediction,& performance Mumbaithrough analytics here
    14. 14. But it’s not only about sensemaking in crisis situations. It can be far more mundane.
    15. 15. Domains of Sensemaking
    16. 16. The contextWhy and what of analytics?ExamplesColliding ideasOrganizations and the analytics model
    17. 17. Exceptional performance requires tight couplingof people, roles, systems, context, and actions
    18. 18. The problem is that our socio-technical systemsare becoming more technical and less social
    19. 19. To avoid getting lost in the “mass of inconsequential” (Bush, 1945)We still need control, but the points are different thanwhere the education system has assigned them in thepast.
    20. 20. How can centralized aims be achieved throughdistributed means?
    21. 21. The current state of work & the internet isantagonistic to existing practices inorganizationsInternet/mobiles/web fragmentsCoherence is needed for action/performance
    22. 22. In response, systems have become more rule oralgorithmically basedSocial systems don’t scale with information’sabundance/complexity
    23. 23. Hence, analytics
    24. 24. http://www.solaresearch.org/
    25. 25. “Imagination no longer comes as cheaply as itdid in the past. The slightest move in the virtuallandscape has to be paid for in lines of code.” Latour (2007)
    26. 26. Learning analytics is the measurement,collection, analysis and reporting of data aboutlearners and their contexts, for purposes ofunderstanding and optimizing learning and theenvironments in which it occurs
    27. 27. The contextWhy and what of analytics?ExamplesColliding ideasOrganizations and the analytics model
    28. 28. http://www.plosone.org/article/info%3Adoi%2Aggregated networks of daily contacts F10.1371%2Fjournal.pone.0023176
    29. 29. What if the topics of interaction are layered onto social networks? http://www.plosone.org/article/info%3Adoi%2Aggregated networks of daily contacts F10.1371%2Fjournal.pone.0023176
    30. 30. Or actions (outcomes, sales, performance) are layered onto networks, discourse, etc. http://www.plosone.org/article/info%3Adoi%2Aggregated networks of daily contacts F10.1371%2Fjournal.pone.0023176
    31. 31. Distributed, multi-level analytics Suthers & Rosen (2011)
    32. 32. Attention metadata Duval (2011)
    33. 33. Learning networks, crowds, communities Haythornthwaite (2011)
    34. 34. Discourse analysis (automated and manual) De Liddo & Buckingham Shum (2011)
    35. 35. Social learning analytics Buckingham Shum & Ferguson (2011)
    36. 36. Participatory learning and reputation Clow & Makriyannis (2011)
    37. 37. Early warning Macfayden & Dawson (2010) Campbell et al (2006)
    38. 38. Learning Analytics in the workplace: Detectingand Analyzing Informal Workplace Learning Schreurs & De Laat, 2012
    39. 39. “Whether from government transparency initiatives, leaks orFreedom of Information requests, journalists are drowning in moredocuments than they can ever hope to read.We’re building an interactive system where computersdo the visualization, while a human guides theexploration.” http://overview.ap.org/about/
    40. 40. The contextWhy and what of analytics?ExamplesColliding ideasOrganizations and the analytics model
    41. 41. Half-ideas colliding to form new (innovative) knowledge wholes: Massive Open Online Courses
    42. 42. Creating: artifacts, stuff, remixing,new assemblies, novel connectedness
    43. 43. Stigmergic/self-organizing social systems
    44. 44. Synchronization
    45. 45. Capacity for innovation & change a function ofwhich/how entities are networked
    46. 46. The contextWhy and what of analytics?ExamplesColliding ideasOrganizations and the analytics model
    47. 47. What might we want to predict?Composition of teamsSkillsets needed for particular outcomesResilience and adaptive teams in unsettledcontexts (rapidly changing)Trends (for re/up skilling)
    48. 48. Kron, et al (2011)
    49. 49. Sensemaking, prediction, performance
    50. 50. www.lakconference.org
    51. 51. Learning Analytics: Practitioners WorkshopPurdue: Oct 1-3, 2012http://www.solaresearch.org/flare/
    52. 52. gsiemens @ gmail Twitter Skype FB Whereverwww.elearnspace.orgwww.connectivism.cawww.learninganalytics.net

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