Jupyter for Education: Beyond Gutenberg and Erasmus

1. Jupyter for Education:   Beyond Gutenberg and Erasmus 2015-07-25 • Seattle Paco Nathan, @pacoid  O’Reilly Learning

2. Who We Are:

3. O’Reilly Learning O’Reilly Learning is a new business unit focused on the (rapid) evolution of learning experiences for our audience, spanning across the range of product offerings at O'Reilly Media

4. Not These People …

5. These People …

6. O’Reilly Learning Objective: Examine, make sense of, and organize   our various training products and learning channels – for ourselves and our customers

7. Content ﬂows through a maze of editorial process, production workﬂows, delivery channels, etc., from authors to audience… Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships O’Reilly Learning

8. Content ﬂows through a maze of editorial process, production workﬂows, delivery channels, etc., from authors to audience… Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships O’Reilly Learning regarded by authors as a relatively “agile” process,   more than most – even so,   it needs much improvement

9. IMHO, here’s the crux of the issue, which impedes the industry in general: Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships O’Reilly Learning Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships

10. 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. Learning Architecture

11. In the words of Michael Pollan, “You are what you eat eats.” michaelpollan.com/reviews/you-are-what-you-eat/ Learning Architecture

12. We live within a community of makers, innovators, learners, implementers… Our objective initially is to provide a learning architecture within our company, leveraging it as a pattern that can help   our customers build their learning architectures, subsequently deployed   on behalf of their customers Authors Audience DB: videos Git: versioning Atlas: publications EPUB oreilly.com Safari On24: webcasts OST: online courses Events Studio: recording SMEs Meetup, etc.: partnerships Learning Architecture

13. Background:

14. 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. Background:

15. Personal experience in 2012-15 as   an independent author and instructor… Just Enough Math  Paco Nathan  O’Reilly Media (2014)  http://justenoughmath.com Background:

16. Personal learnings, based on working   on this project with Kyle and Andrew… How to transit from the role of data scientist, software developer, engineering director –   into a role of author, teacher and vice versa Background:

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

18. Embracing Jupyter Notebooks at O'Reilly  Andrew Odewahn, 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, Docker, etc. Background:

19. Embracing Jupyter Notebooks at O'Reilly Andrew Odewahn 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 Background:

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

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

22. Background: Beta is our proof of concept: https://beta.oreilly.com/learning

23. Tech Stack: production presentation Thebe: player Jupyter: notebook Docker: container web page: interaction Git: versioning Atlas: publications various formats authoring cloud infra

24. Question: What’s the delta between our current   author workﬂow and this new world of   Jupyter + Docker +Thebe + cloud, etc.? production presentation Thebe: player Jupyter: notebook Docker: container web page: interaction Git: versioning Atlas: publications various formats authoring cloud infra

25. Great Examples:

26. Great Examples: Seeing what Microsoft is doing with Jupyter notebooks in Cortana Analytics – that’s brilliant http://gallery.azureml.net/Experiment/3fe213e3ae6244c5ac84a73e1b451dc4

27. Most deﬁnitely check out CodeNeuro, both online and the conf/hackathon…   for example: 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:

28. Curating a list of examples, as a shared doc online, and some exemplars include… Lorena Barba, GWU  http://lorenabarba.com/ Anita Raichand  https://github.com/painterly/data_py Chris Fonnesbeck,Vanderbilt  https://plot.ly/ipython-notebooks/computational-bayesian- analysis/ Donne Martin, NemetschekVectorworks  https://bit.ly/data-notes Great Examples:

29. Compare/contrast Jupyter with other interesting notebooks impls… Databricks  https://class01.cloud.databricks.com/#notebook/76328 R Markdown  http://rmarkdown.rstudio.com/ Andy Petrella, Data Fellas  https://github.com/andypetrella/spark-notebook IBM Knowledge Anyhow  https://knowledgeanyhow.org Mathematica  https://www.wolfram.com/learningcenter/tutorialcollection/ NotebooksAndDocuments/ Great Examples:

30. Learning:

31. A few features on the wish list for notebooks: • integrating video content • social aspects, collaboration • a spectrum of learning modes engaged • how to integrate classroom experience • expert mentoring • learning paths • remote learning environments, e.g., massive open online somethingorother Learning meets Data Science:

32. MOOCs, such as edX, provide excellent features for learning at scale, however: • costly for authors producing content • difﬁcult to instrument • relatively low ROI (completion rates)  Typesafe as a rare counterexample • lacking social context that reinforces learning … it’s difﬁcult to staff a   small army of TAs who are needed What about MOOCs?

33. Peter Norvig @ Future Learning 2020 Summit, 2015-05-30: • search engines surface too many choices for available learning content • (“Thanks Google”) • need to get people to want to interact with the material – generally due to social context What about MOOCs?

34. Signiﬁcant improvement in the notion   of “ﬂipped” a.k.a. inverted classrooms For a good example, see: 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 Learning meets Data Science:

35. There are other pedagogical issues to address, e.g., how to differentiate which content or mode will be most effective   for a learner’s needs and learning style Patterns of Code as Media  Andrew Odewahn, O’Reilly Media  odewahn.github.io/patterns-of-code-as-media/www/ introduction.html Learning meets Data Science:

36. 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? Learning meets Data Science: BTW, did we mention the intense needs   for data analytics at scale and, in particular, dimensional reduction? :)

37. Education is more than lessons, exams, certiﬁcations, instructor evals, etc., …   though tooling often reduces it to that level Is it possible to measure the “distance” between a learner and the 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 Learning meets Data Science:

38. Learning Curves are forever – In some sense, this is essence   of Data Science:   How well do you learn? In my experience, much of the risk encountered in managing a Data Science team is about budgeting for learning curve Learning meets Data Science:

39. ThrowYour Life a Curve  Whitney Johnson blogs.hbr.org/johnson/2012/09/ throw-your-life-a-curve.html For example, notions of continuous learning: • deconstruction of the cognitive bias One Size Fits All • “makes a compelling case for personal disruption” • “plan your career around learning curves” • hire people who learn/re-learn efﬁciently Learning meets Data Science:

40. So who (or where) are the experts in this graph?! Diffusion of Innovation  Everett M. Rogers (1962)  http://sphweb.bumc.bu.edu/otlt/MPH-Modules/SB/ SB721-Models/SB721-Models4.html Learning meets Data Science:

41. Looking Ahead:

42. Moving beyond books, beyond Kindle, beyond MOOCs … Moving forward, important aspects include: learning paths, continuous learning, inverted classroom, computational thinking, learner segmentation, etc. Also, it’s not so much about how an individual learns, rather our focus should include social context, e.g., learning within   a team Looking Ahead:

43. Moving beyond books, beyond Kindle, beyond MOOCs Moving forward, important aspects include: learning paths classroom segmentation Also, it’s not so much about how an individual learns, rather our focus should include a team Looking Ahead: we’re eager to work with great new notebook authors!!  #pioneers

44. Thank You!