Jupyter / IPython Notebook Textbook Companions

I still remember the first time I was introduced to Jupyter (then IPython) notebooks – a demonstration at the back of a large lecture room by Alfred Essa of his “Rwandan Tragedy” notebook:

(I think this was a OpenEd12 in Vancouver (“Beyond Content”), back in the days when I used to blag entry to conferences, somehow or other, so this would date it to October 2012…).

I don’t recall offhand what my immediate reaction was (I’d like to think it was unbridled enthusiasm, but I’m not sure I completely grokked it…). A scan of my laptop (commissioned over summer 2013?) shows I have notebook files dating back to at least December 2013 (Jan 2014 on Github gists), at which point I seemed to really start playing…

In that period of time, I suspect things had moved on quite a bit since seeing the Rwanda demo, such as the embedding of output charts in the notebooks. (Of course, now you can embed, and generate embeddings, of pretty much anything, as well as being able to easily add in interactive widgets into a notebook to control embedded interactives using code defined in the same notebook…)

So it seems it took me some time to starting to explore them. But when I did start to use them, there was no going back…

I also remember meeting Alistair Willis in the OU Berrill café, mooting plans for the course that was to become TM351 (the first module team meeting was October 2013, perhaps?) , and at some point the idea of using IPython notebooks for the course came up, though I’m not sure I had any experience of using the notebooks – just that they seemed like something worth exploring…

Alistair quickly became a fellow early believer in the notebooks, and since then, the FutureLearn course Learn to Code for Data Analysis, led by Michel Wermelinger, has also used them.

But that, to my knowledge, and to my shame, is as far as it’s gone in the OU.

The new first year equivalent introductory computing courses use Scratch (I tried to argue for BlockPy, but the decision had been made to go with what had been used before….) and IDLE, and whilst there was some talk of Python coding in the new level 2 and/or level 3 Engineering courses, I’m not sure how that’s progressed.

I’ve no idea what OUr Science courses are up to – or Maths courses. Or courses that use statistics. Or interactive maps.

Anyway, over the last few years I’ve come to live in Jupyter notebooks – they’re great for trying stuff out, keeping records of play and experiments in a way that the interactive command line isn’t (even if you do save all your history files), and can be used for sharing complete, worked, and working recipes.

As my own timeline suggests, being aware of the notebooks and actually buying into them, takes – using them. Which is maybe one reason why adoption in the OU has been slow: fear of the new.

Which is a shame – because there’s a great ecosystem developing around the use of notebooks.

For example, yesterday, whilst search for “tractive effort gearing ipynb”, trying to find notebook examples of tractive effort curves (a phrase I picked up from Racecar Engineering mag – race engineers cut their teeth on the maths of finding gear ratios by calculating such curves, apparently…) I came across this notebook file:

Not rendered, but that’s easy enough using nbviewer:

which gives this:

Hmmm… a set of worked examples from a textbook. What textbook?, I wondered, and went up the URL path:

Interesting… So how active a project is this?

Hmm… really interesting… The examples may be pared down, but that means they can also be worked up. (It look like there’s a Github repo, which I guess you can fork and then make pull requests back to with worked examples for new books, or improved notebooks for current ones?) And they show how to go about solving a wide range of problems by scripting them. (This is one reason why I think computing folk don’t like notebooks. They aren’t really interested in folk using simple scripting to get simple things done. Which is also the reason why computing folk are the worst people to try to teach computing to the masses, who don’t know code can be used, a line at a time, to get things done, and who don’t see the point in being taught the stuff that the computing folk want to teach. Which is old school computing principles, rather than TECHNOLOGY THAT’S USEFUL TO FOLK.)

Whatever…. As a “digital first” organisation, I keep wondering why we’re not buying into Jupyter as a piece of freakin’ awesome edtech?! (By the by, a history of the IPython notebook project can be found here.)

If nothing else, I’d be really interested to see research from OUr digital innovation leaders why there’s no interest in adopting Jupyter notebooks in the institution?

See also: I Just Don’t Understand Why…

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