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From data to decisions

From data to decisions

2013 talk about how Khan Academy mucks through messy data to make decisions

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Ben Kamens

May 21, 2013

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Transcript

  1. A history in 60 seconds 60 59 58 57 56

    55 54 53 52 51 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 BZZZzzzzzzzzz! Please yell, “SHUT UP, BEN.”
  2. Epic results Messy reality Messy reality Epic results Messy reality

    Messy reality Messy reality Messy reality Messy reality Messy reality
  3. Old struggling New struggling - vs - 30 attempts, still

    no mastery Emphasize last few problems
  4. “the difference between theory and practice is greater in practice

    than in theory” “the reality is that most ideas fail to move the metrics they were designed to improve”
  5. “the difference between theory and practice is greater in practice

    than in theory” “the reality is that most ideas fail to move the metrics they were designed to improve” “weeks to months to properly analyze and get to the often surprising root cause.”
  6. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Retention? Same. Videos finished? Same.
  7. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Problems done? Same. Retention? Same. Videos finished? Same.
  8. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Problems done? Same. Problems answered correctly? Same. Retention? Same. Videos finished? Same.
  9. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Problems answered correctly? Same. Retention? Same. Videos finished? Same.
  10. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Discussion participants? Stat sig. Problems answered correctly? Same. Retention? Same. Videos finished? Same.
  11. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Discussion participants? Stat sig. Problems answered correctly? Same. Retention? Same. Videos finished? Same.
  12. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Discussion participants? Stat sig. Problems answered correctly? Same. Retention? Same. Videos finished? Same. Users who started registering but didn’t finish?
  13. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Discussion participants? Stat sig. Problems answered correctly? Same. Retention? Same. Videos finished? Same. Problems answered incorrectly? Users who started registering but didn’t finish?
  14. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Discussion participants? Stat sig. Problems answered correctly? Same. Retention? Same. Videos finished? Same. Problems answered incorrectly? Users who started registering but didn’t finish? Updates to profiles?
  15. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Discussion participants? Stat sig. Problems answered correctly? Same. Retention? Same. Videos finished? Same. Problems answered incorrectly? Users who started registering but didn’t finish? Updates to profiles? Number of email subscriptions?
  16. A versus A Signups? Same. Skills mastered? Same. Videos started?

    Same. Visits? Same. Profiles made? Same. Problems done? Same. Discussion participants? Stat sig. Problems answered correctly? Same. Retention? Same. Videos finished? Same. Problems answered incorrectly? Users who started registering but didn’t finish? Updates to profiles? Users voting on discussion posts? Number of email subscriptions?
  17. “Bing had a bug in an experiment, which resulted in

    very poor search results...” “revenue per user went up over 30%!”
  18. Old “strict” proficiencies New “early” proficiencies - vs - Very

    strict proof of proficiency Use more data, be less strict
  19. I am so jealous of customer lifetime value. I am

    so jealous of customer lifetime value. student learning.
  20. “The more you learn today, the smarter you’ll be tomorrow.”

    5% more problems attempted 5% more skills mastered
  21. “The more you learn today, the smarter you’ll be tomorrow.”

    5% more problems attempted 5% more skills mastered 40,000 data points in days