A placeholder post, as much as anything, to mark the AJET Call for Papers for a Special Issue on Re-Examining Cognitive Tools: New Developments, New Perspectives, and New Opportunities for Educational Technology Research as a foil for thinking about what Jupyter notebooks might be good for.
According the the EduTech Wiki, “[c]ognitive tools refer to learning with technology (as opposed to learning through technology)” which doesn’t really makes sense as as sentence and puts me off the idea of ed-tech academe straight away.
In the sense that cognitive tools support a learning process, I think they can do so in several ways. For example, in Programming in Jupyter Notebooks, via the Heavy Metal Umlaut I remarked on several different ways in which the same programme could be constructed within a notebook, each offering a different history and each representing a differently active approach to code creation and programming problem solving.
One of the things I try to do is reflect on my own practice, as I have been doing recently whilst trying to rework fragments of some OpenLearn materials as reproducible educational resources (which is to say, materials that generate their own resources and as such support reuse with modification more generally than many educational resources).
For example, consider the notebook at https://notebooks.azure.com/OUsefulInfo/libraries/gettingstarted/html/3.6.0%20Electronics.ipynb
You can also run the notebook interactively; sign in to Azure notebooks (if you’re OU staff, you can use your staff OUCU/OU password credentials) and clone my Getting Started library into your workspace. If notebooks are new to you, check out the 1.0 Using Jupyter Notebooks in Teaching and Learning - READ ME FIRST.ipynb notebook.
In creating the electronics notebook, I had to learn a chunk of stuff (the lcapy package is new to me and I had to get my head round circuitikz) but I found trying to figure out how to make examples related to the course materials provide a really useful context for giving me things to try to do with the package. In that the sense, the notebook was a cognitive tool (I guess) that supported my learning about lcapy.
For the https://notebooks.azure.com/OUsefulInfo/libraries/gettingstarted/html/1.05%20Simple%20Maths%20Equations%20and%20Notation.ipynb notebook, I had to start getting my head round sympy and on the way cobble together bits and pieces of code that might be useful when trying to produce maths related materials in a reproducible way. (For example, creating equations in sympy that can then be rendered, manipulated and solved throughout the materials in a way that’s appropriate for a set of educational (that is, teaching and/or learning) resources.
Something else that came to mind is that the notebook medium as both an authoring medium and a delivery medium (we can use it just to create assets; or we can also use it deliver content to students) changes the sorts of things you might want to do in the teaching. For example, I had the opportunity to create self test functions, and there is the potential for interactives that let students explore the effect of changing component values in a circuit. (We could also plot responses over a range of variable values, but I haven’t demoed that yet.) In a sense, the interactive affordances of the medium encouraged me to think of opportunities to create philosophical instruments that allow authors – as well as students – to explore the phenomena being described by the materials. Although not a chemistry educator, putting together a reworking of some OpenLearn chemistry materials – https://notebooks.azure.com/OUsefulInfo/libraries/gettingstarted/html/3.1.2%20OpenLearn%20Chemistry%20Demos.ipynb – gave me some ideas about the different ways in which the materials could be worked up to support interactive / self-checking /constructive learning use. (That is, ways in which we could present the notebooks as interactive cognitive tools to support the learning process on the one hand, or as philosophical instruments that would allow the learner explore the subject matter in an investigative and experimental way.)
I like to think the way I’m using the Jupyter notebooks as part of an informal “reproducible-OER” exploration is in keeping with some of the promise of live authoring using the OU’s much vaiunted, though still to be released, OpenCreate authoring environment (at least, as I understand the sorts of thing it is supposed to be able to support) with the advantage of being available now.
It’s important to recognise that Jupyter notebooks can be thought of as a medium that behaves in several ways. In the first case, it’s a rich authoring medium to work with – you can create things in it and for it, for example in the form of interactive widgets or reusable components such as IPython magics (for example, this interactive mapping magic: https://github.com/psychemedia/ipython_magic_folium ). Secondly, it’s a medium qua environment that can itself be extended and customised through the enabling and disabling of notebook extensions, such as ones that support WYSIWYG markdown editing, or hidden, frozen and read-only executable cells, which can be used to constrain the ways in which learners use some of the materials, perhaps as a counterpoint to getting them to engage more actively in editing other cells. Thirdly, it acts as a delivery medium, presenting content to readers who can engage with the content in an interactive way.
I’m not sure if there are any good checklists of what makes a “cognitive tool” or a “philosophical instrument”, but if there are it’d be interesting to try to check Jupyter notebooks off against them…
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