MOOCs and the Funnel of Participation


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Presentation at LAK13, the Third Conference on Learning Analytics and Knowledge, Leuven, Belgium, April 2013. Full paper and other details at doug.clow/mooc-funnel

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  • I’m following a really great paper, and a really funny person. And I’m standing between you and leaving. We’re all tired. So I’ll be animated and flash lots of things past you really quickly and maybe we can have some talk over beer.
  • First presentation had more data. Although the MOOC was more an OOC – only 200 weren’t disengaging.
  • 600 year old idea like KU Leuven
  • He’s reading about happiness – and it’s a mathematical formula. As we all know, great mathematics gives great happiness.
  • Fountain of Doubt
  • Technical The diversity of online tools often employed can make it hard to collect and process data; the disconnection from formal educational systems means that much useful data - such as demographics, previous experience, other courses being studied, etc - is unavailable. Theoretical To date, MOOCs have not led directly to the award of credit, so analytics in these terms is problematic: endpoints, progress and feedback are different. Philosophical For connectivist MOOCs, the idea of defining learning endpoints ahead of time, let alone tracking and measuring progress towards them, is anathema. Resource An important factor in the rapid spread of MOOCs is their low cost base: this means there is far less resource available per student for analytics activities – and, more importantly, far less for human mediation of analytics feedback
  • Things have changed in last four/five months – FutureLearn is new. A word from my sponsors. New. British accents. Pedagogy. Tell us how to do it.
  • This slide has become outdated during the course of this session.
  • This *was* the state of the art.
  • One of our graduate students has done the job properly. The scale only goes up to 20%.
  • Buy a degree gag Big drop each time
  • Drop off each time
  • Highly unequal, steeply unequal. A few people do lots, lots of people do a little. It can be exponential fall off – or steeper than that. But IT’S NOT A POWER LAW. You need a lot of data.
  • Three examples. See the drop at each stage.
  • Compare those to 7% dropout … but this is the people who started at all.
  • Steep drop off, highly unequal. A few students visiting thousands of times, most visit a few times … but …
  • Again, one or two posted hundreds of forum posts., hundreds posted once. (this is starting new threads)
  • This took so much effort to make – no way should it have been so hard to collate visit data to forum posts. But it was a bloody nightmare. And then I chopped it from my paper for space reasons. So just look at it for a minute, in honour of all the work that went in to it. But it tells us little so we move on.
  • Stanford/Oxford/MIT. Community college, post-92. OU, U Phoenix. The traditional universities – mass, distance/open/online - are doing something right that MOOCs are not doing. All that work on student support is not wasted. Commitment too. We can’t not care about drop out.
  • But not a power law!
  • MOOCs and the Funnel of Participation

    1. 1. MOOCs and theFunnel of Clow, Institute of Educational Technology,The Open University, UKThird International Conference onLearning Analytics and Knowledge LAK13, April 2013, Leuven
    2. 2. Overview• MOOC dropout• This is not new• Here is some actual empirical data on a MOOC• Dangerous aim: To change the way you think about MOOCs and dropout. With a funnel. Photo (cc) Nina Hale
    3. 3. Nuremberg funnel,1902
    4. 4. Just say no!to antediluvian,ineffective,unsophisticaedepistemologies ofpedagogicalontology
    5. 5. “Fonske” Fons sapientiae Fountain of KnowledgePhoto (cc) Dogfael
    6. 6. Photo (cc) Dogfael
    7. 7. Cartoon (c) Oglaf
    8. 8. • What’s a MOOC? xMOOC vs cMOOC • “Officially the buzzword of 2012” • Field moving very fast • Learning analytics on MOOCs is hard: – Technical challenges – Theoretical challenges – Philosophical challenges – Resource challenges Honeybees Apis melliferaPhoto (cc) David Goehring
    9. 9.
    10. 10. Main findings of MOOC research • People drop out • People drop out a lot • More research required • People really do drop out a lot Photo (cc) jeroen bennink
    11. 11. State of the art in MOOC Completion Rates Nov 2012 CCK08 PLENK2010 MITx Coursera Circuits & Software Electronics Engineerin gRegistered 2,200 1,641 150,000 50,000Completed At least 16 40-60 7,157 3,500 ‘active’Competion Not Not 7% 7%rate definable definable Photo (cc) Seth Tisue
    12. 12. MOOC Completion Rates April 2013
    13. 13. Awareness Interest Desirethe marketing funnel Purchase
    14. 14. Awareness Registration Activitythe funnel ofparticipation Progress
    15. 15. steep drop off from one stage to the nextfunnel of participation = + highly unequal participation pattern
    16. 16. it’s not a power law
    17. 17.   iSpot Cloudworks openEDVisits 1,100,000 275,000 30,000Unique visitors 390,000 165,000 15,500 (35%) (60%) (52%)Registrations 21,000 5,239 1,429 (5%) (3%) (9%)Contributors 9,000 1,750 198 (43%) (33%) (14%) Data from respective site launch to 7 November 2012
    18. 18.   iSpot Cloudworks openEDVisits 1,100,000 275,000 30,000Unique visitors 390,000 165,000 15,500 (35%) (60%) (52%)Registrations 21,000 5,239 1,429 (5%) (3%) (9%)Contributors 9,000 1,750 198 (43%) (33%) (14%) Data from respective site launch to 7 November 2012
    19. 19. •• Free, open, online, OER, CC BY-SA• Seven EU partners• Three presentations, 2009-2012• Joomla, email, IRC, Elluminate• Explored business models, found none
    20. 20. Log plot of the number of visits to openED per user, ranked by number of visits.
    21. 21. it’s not a power law
    22. 22. Log plot of the number of forum posts on openED per user, ranked by number of forum posts
    23. 23. it’s not a power law
    24. 24. Visits vs forum posts made by individual openED users R = 0.86, p < 0.0001
    25. 25. Distance / Elite Mass open / university university online MOOCs universityRepresentativecompletion rate ~ 90% ~ 60% ~ 35% < 10% Photo (cc) Walt Hubis
    26. 26. steep drop off from one stage to the nextfunnel of participation = + highly unequal participation pattern
    27. 27. Put your thinking funnel on • Think less about total dropout • Think more about why participation reduces at each stage • Think about patterns of participation • Benchmark whether it’s a problem • Pour more in the top, or widen the funnelPhoto (cc) Tom Bayly
    28. 28. Thanks for support from:•James Aczel, Simon Cross,•openED partners and learners• everyone who gave me data• European Commission funding for openED project 505667-LLP-1-2009-1-PT-KA3-KA3MP Photo (cc) wales_gibbons
    29. 29. Doug This work is licensed under a Creative Commons Attribution 3.0 Unported License