Data Science Reinvents Learning?

1. 2015-08-24 • San Jose Paco Nathan, @pacoid  Director, O’Reilly Learning Data Science Reinvents Learning? Beyond Gutenberg and Erasmus meetup.com/SF-Bay-ACM/events/221693508/

2. 2 Some Background… • O’Reilly Learning: you may only hear about us in   a few instances, if we do our job well; ACM is a great forum for this discussion • prior: built-out the community evangelism and training program for Apache Spark at Databricks • prior: led Data teams for several years, working on   large-scale ML apps in industry, including: one of the largest Hadoop instances running in AWS (2008);   one of the ﬁrst 100% AWS system architectures (2006) • … • ancient prior: Stanford CSD teaching fellowship (1984-86, Alice Supton, Stuart Reges) peer-teaching CS course which later became Residential Computing

3. WWSVD?

4. 4 Intro Quite candidly, the one common catch phrase   in SiliconValley that I ﬁnd most terrifying: “It’s like Uber, for ___”

5. 5 Intro Ostensibly that leads to a question, how might   an “Uber for Education” look?

6. 6 Intro Ostensibly that leads to a question, how might   an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that

7. 7 Intro Ostensibly that leads to a question, how might   an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that

8. 8 Intro Ostensibly that leads to a question, how might   an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that b) Would we really need that anywho?

9. 9 Intro Ostensibly that leads to a question, how might   an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that b) Would we really need that anywho? c) Uber itself might not take that approach …

10. 10 Intro Ostensibly that leads to a question, how might   an “Uber for Education” look? a) Similar to Cthulhu, we might regret actually seeing that b) Would we really need that anywho? c) Uber itself might not take that approach … Perhaps “Uber for Learning” might be somewhat  more apt? In any case, what comes after Books, Kindle, MOOCs?

11. 11 Some Definitions… “Learning” ergo… “Education” ergo… “School”

12. “Learning” ergo… “Education” ergo… “School” X 12 Some Definitions… Schools are great to have… If you need a school, pick a   good one and go To be clear, we’re not a school

13. 13 Some Definitions… Even the best schools these days question  what they will become in 5-10 years Not-so-best schools are perhaps questioning   much more than that

14. 14 Some Definitions… Oh BTW, too many (funded) teams seem to   have this mediocre idea for “education”: 1. assessment: collect test scores ➜ 2. define “quantified student” ➜ 3. reuse online marketing funnel ad-tech ➜ 4. invoke agile coding teams ➜ 5. ship mobile/cloud-based SaaS platform ➜ 6. ... 7. profit

15. Oh BTW, too many (funded) teams seem to have this mediocre idea for “education” 1. assessment: collect test scores 2. define “quantified student” 3. reuse online marketing funnel ad-tech 4. invoke agile coding 5. ship a mobile/cloud-based SaaS platform 6. ... 7. profit 15 Some Definitions… LMS

16. K-12 not so much, except perhaps in the case of Safari for Schools undergrad textbooks? graduate textbooks, conferences? professional focus of our audience 16 Some Definitions…

17. 17 • vocational:   making a career move • aspirational:   improvement within a career path • proﬁciency:   has a speciﬁc pain-point, needs to resolve it • familiarity:   wants to join in a team dialog about a topic,   e.g., conversational programmer Learner Personas for professional category

18. What about MOOCs?

19. 19 What about MOOCs? Massive Open Online Courses –   seven year trend, beginning with: Connectivism and Connective Knowledge  George Siemens, Stephen Downes  University of PEI (2008)  http://cck11.mooc.ca/

20. 20 What about MOOCs?

21. 21 What about MOOCs? Anthony Joseph  UC Berkeley early Jun 2015 edx.org/course/uc-berkeleyx/uc- berkeleyx-cs100-1x- introduction-big-6181 Ameet Talwalkar  UCLA late Jun 2015 edx.org/course/uc-berkeleyx/ uc-berkeleyx-cs190-1x- scalable-machine-6066

22. 22 What about MOOCs? Pros: • cost-effective to reach a large audience • popular with students • ¿ addresses “train the trainers” bottleneck ? Cons: • expensive to produce and curate • most students are sampling • low completion rates • somewhat chaotic • lecture fatigue • ¿ reinforces advantage of the elites ?

23. 23 What about MOOCs? Online education: MOOCs taken by educated few  Ezekiel Emanuel, Nature 503, 342 (2013-11-21) • 80% students already have an advanced degree • 80% come from the richest 6% of the population Michael Shanks @Stanford: retrenchment around traditional disciplines will make disparities even more pronounced An Early Report Card on Massive Open Online Courses  Geoffrey Fowler, WSJ (2013-10-08) Amherst, Duke, etc., have rejected edX see: Open edX Universities Symposium @GWU, 2015-11-11

24. 24 • search engines surface too many choices   among the available learning content • we must get people wanting to interact with the material – generally due to social context • academe strives to decontextualize, which   is the opposite of learning in context • how do we recognize that learning has occurred? • what is the learning promise? What about MOOCs?

25. Examples for Consideration

26. 26 Introduction to Robotics Peter Corke @QUT https://moocs.qut.edu.au/learn/introduction-to- robotics-august-2015 • effective use of peer review for scaling • worked well reaching into Africa, India Peer Review

27. 27 EffectiveThinkingThrough Mathematics Michael Starbird @UT/Austin https://www.edx.org/course/effective-thinking- through-mathematics-utaustinx-ut-9-01x • getting students to articulate their epiphany moments is more interesting   than other results – Donna Kidwell Epiphany Moments

28. 28 Caltech Offers Online Course with   Live Lectures in Machine Learning Yaser Abu-Mostafa (2012-03-30) http://www.caltech.edu/news/caltech-offers-online- course-live-lectures-machine-learning-4248 • signiﬁcant improvement through the use of “ﬂipped” a.k.a. inverted classrooms Inverted Classrooms

29. 29 Scalable Learning  David Black-Schaffer @Uppsala  Sverker Janson @KTH SICS https://www.scalable-learning.com/ • active learning: Flipped Classroom and Just-in-timeTeaching • exams built directly into speciﬁc diagrams within videos • metrics for where in video+code that students get stuck • instructor can customize subsequent classroom discussions   (active teaching phase) based on stuck/unstuck metrics Inverted Classrooms

30. 30 How to Flip a Class   CLT @UT/Austin  http://ctl.utexas.edu/teaching/ﬂipping-a-class/how 1. identify where the ﬂipped classroom model makes   the most sense for your course 2. spend class time engaging students in application activities with feedback 3. clarify connections between inside and outside   of class learning 4. adapt your materials for students to acquire course content in preparation of class 5. extend learning beyond class through individual   and collaborative practice Inverted Classrooms

31. 31 Learning programming at scale Philip Guo   O’Reilly Radar (2015-08-13) http://radar.oreilly.com/2015/08/learning- programming-at-scale.html • PythonTutor • Codechella Tutors could keep an eye on around   50 learners during a 30-minute session,   start 12 chat conversations, and   concurrently help 3 learners at once Collaborative Learning

32. 32 Data-driven Education and the Quantiﬁed Student Lorena Barba @GWU PyData Seattle 2015 https://youtu.be/2YIZ2SY9mW4 • keynote talk: abstract, slides • homepage If you study just one link in this entire talk…

33. Project Jupyter

34. 34 If by some bizarre chance you haven’t used   it already, go to https://jupyter.org/ • 50+ different language kernels • new funding 2015-07 • UC Berkeley, Cal Poly • nbgrader autograder by Jess Hamrick • jupyterhub multi-user server • curating a list of examples • repeatable science! see also:  Teaching with Jupyter Notebooks  http://tinyurl.com/scipy2015-education Project Jupyter

35. 35 Deploying JupyterHub for Education  Jessica Hamrick  Rackspace blog (2015-03-24)  https://developer.rackspace.com/blog/deploying- jupyterhub-for-education/ Project Jupyter

36. 36 Literate Programming  Don Knuth  Univ of Chicago Press (1992)  literateprogramming.com/ Instead of imagining that our main task is   to instruct a computer what to do, let us  concentrate rather on explaining to human  beings what we want a computer to do Evoking some earlier works…

37. 37 Most deﬁnitely check out CodeNeuro, both online and the conf/hackathon… Some great examples: Jeremey Freeman, HHMI Janelia Farm  http://notebooks.codeneuro.org/ Matthew Conlen, NY Data Company  http://lightning-viz.org/ Olga Botvinnick, UCSD  http://yeolab.github.io/ﬂotilla/docs/gallery/ Great Examples

38. 38 http://mybinder.org/ turn a GitHub repo into a collection   of interactive notebooks powered by Jupyter and Kubernetes Launch Vehicles

39. Jupyter, Thebe, Atlas, Docker

40. 40 Embracing Jupyter Notebooks at O'Reilly  Andrew Odewahn  O’Reilly Media (2015-05-07) https://beta.oreilly.com/ideas/jupyter-at-oreilly O’Reilly Media is using our Atlas platform   to make Jupyter Notebooks a ﬁrst class authoring environment for our publishing program Jupyter, Thebe, Atlas, Docker, etc. Content Toolchain

41. 41 Embracing Jupyter Notebooks at O'Reilly Andrew Odewahn O’Reilly Media (2015-05-07) https://beta.oreilly.com/ideas/jupyter-at-oreilly O’Reilly Media is using our Atlas platform to make Jupyter Notebooks a ﬁrst class authoring environment for our publishing program Jupyter Content Toolchain

42. 42 On Demand Analytic and Learning Environments with Jupyter  Kyle Kelley, Andrew Odewahn  lambdaops.com/jupyter-environments-odsc2015/ Exploring a couple themes, in particular: • computational narratives - exploratory data analysis - software development/collaboration - API exploration - technical papers - reports, exec dashboards • code-as-media - Thebe project, etc. Content Toolchain

43. 43 Personal experiences during 2012-2015   as an author and instructor… Just Enough Math  Paco Nathan  O’Reilly Media (2014)  http://justenoughmath.com Content Toolchain

44. 44 Learnings based on working on this project with Kyle and Andrew… How to transit from roles of data scientist, software developer, engineering director –   into roles of author, teacher – and vice versa Content Toolchain

45. 45 Interactive notebooks:   Sharing the code Helen Shen Nature (2014-11-05) nature.com/news/interactive-notebooks- sharing-the-code-1.16261 Content Toolchain

46. 46 Content Toolchain Atlas is our content platform backed by Git, for project collaboration among authors, editors, et al. https://atlas.oreilly.com/

47. 47 Content Toolchain Thebe (a moon of Jupiter) provides a layer atop Jupyter that is needed for publishing, white-labeled content, etc. https://github.com/oreillymedia/thebe

48. 48 Content Toolchain Beta is our new site design: https://beta.oreilly.com/learning

49. 49 Content Toolchain Contrast our current talent workﬂow and this   new world of Jupyter+Docker+Thebe+cloud … How would it work with known successes such   as Head First? production presentation Thebe: player Jupyter: notebook Docker: container web page: interaction Git: versioning Atlas: publications various formats authoring cloud infra

50. Does Science begin with Phenomenology?

51. 51 Audience Patterns for Learning: ad-hoc

52. 52 Audience Patterns for Learning: architecture events inverted on-demand Mostly Synchronous Mostly Asynch Inverted Classroom Paywall Subscription Free Content

53. 53 The Learning Architecture: Deﬁning Development and Enabling Continuous Learning David Mallon, Dani Johnson Bersin (2014-05-06) http://www.bersin.com/Practice/Detail.aspx? docid=17435&mode=search&p=Learning-@-Development This report is designed to help leaders   and talent development and learning   professionals to take positive steps   toward understanding and implementing   learning architectures Sidebar: Learning Architecture

54. Think of a favorite open source framework … who (or where) are the experts in this graph? Sidebar: Innovators vs. Experts Diffusion of Innovation  Everett Rogers (1962)  http://sphweb.bumc.bu.edu/otlt/MPH-Modules/SB/SB721- Models/SB721-Models4.html 54

55. 55 Building Blocks In software engineering, we rarely hand a   developer the spec for some app and say   “Start from scratch, then come back when you’re done.” Instead: • focus on MVP • leverage APIs, libraries, microservices, etc. • iterate on small, incremental changes • this allows for TDD, CI, etc. • plus, customer experiments ➜ data science Compare/contrast that with how publishers approach authors, speakers, instructors?

56. 56 Building Blocks Proposing a new format spec to replace   EPUB, MOBI, etc.: • video segments + transcripts • notebooks in Jupyter+Thebe+Docker • metadata (persona, topics, cues, etc.) • links to Git repos, Dat data • annotations atop existing content • webcast/livestream • social interaction (TA/mentoring) • evaluation modules • discourse analytics most reused across a spectrum of synchronous to async instrumented for experiments, analytics, iteration

57. 57 total newbie good overview Do you have sufﬁcient familiarity with the topic? utterly confused familiar territory Can you build on familiarity with a related topic? must get unstuck send pull request Do you have necessary proﬁciency in the topic? learner topic experience concise topic inter- disciplinary How many boundaries must you span to achieve structural literacy for this topic? want to for myself have to for my job What is your primary motivation to learn this topic? bleeding edge COBOL 2020 Where are you on the "diffusion of innovation" curve w.r.t. the topic? on- demand major event How high is the transaction cost for the experience delivered to you? "go read the code" full-team participation Does the learning experience immerse you within a diverse, supportive social context? Dimensional Reduction Did we mention intense needs   for data analytics at scale?

58. 58 Is it possible to measure “distance” between   a learner and a subject community? From Amateurs to Connoisseurs:  Modeling the Evolution of User   Expertise through Online Reviews  Julian McAuley, Jure Leskovec  http://i.stanford.edu/~julian/pdfs/www13.pdf Recommender Systems

59. 59 Back to “Uber for Learning” – approaching from a learner (audience) perspective, generally within a social context Given that: • books aren’t used by learners as much anymore • experts don’t have time to write books anymore If we can: • ﬁt learners’ needs to topics w.r.t. subject communities,   based on their S-curve positions • personalize lectures for learners’ pain-points • reuse containerized building blocks Imagine the extent to which our current data science   tooling and techniques can be leveraged? Summary

60. 60 PS: If you are interested in opportunities   to write, speak, teach, mentor, code, etc.,   based on these approaches, let us know Get Involved!

61. Thank You! and Stay Tuned…

62. presenter: Just Enough Math O’Reilly (2014) justenoughmath.com monthly newsletter for updates,   events, conf summaries, etc.: liber118.com/pxn/

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