Scratch Materials – Using Blockly Style Resources in Jupyter Notebooks

One of the practical issues associated with using the Scratch desktop application (or it’s OU fork, OUBuild) for teaching programming is that runs on the desktop (or perhaps a tablet? It’s an Adobe Air app which I think runs on iOS?). This means that the instructional material is likely to be separated from the application, either as print or as screen based instructional material.

OUBuild

If delivered via the same screen as the application, there can be a screen real estate problem when trying to display both the instructional material and the application.

In OU Build, there can also be issues if you want to have two projects open at the same time, for example to compare a provided solution with your own solution, or to look at an earlier project as you create a new one. The solution is to provide two copies of the application, each running its own project.

Creating instructional materials can also be tricky, requiring the capturing of screenshots from the application and then inserting them in the materials, along with the attendant risk when it comes to updating the materials that screenshots as captured in the course materials may drift from the actuality of the views in the application.

So here are a couple of ways that we might be able to integrate Scratch like activities and guidance into instructional materials.

Calysto/Metakernel Jigsaw Extension for Jupyter Notebooks

The Calysto/Metakernel* Jigsaw extension for Jupyter notebooks wraps the Google Blockly package for use in a Jupyter notebook.

Program code is saved as an XML file, which means you can save and embed multiple copies of the editor within the same Jupyter notebook. This means an example programme can be provided in one embed, and the learner can build up the programme themselves in another, all in the same page.

The code cell input (the bit that contains the %jigsaw line) can be hidden using the notebook Hide Input Cell extension so only the widget is displayed.

The use of the editor is a bit tricky – it’s easy to accidentally zoom in and out, and I’m guessing not very accessible, but it’s great as a scratchpad, and perhaps as an instructional material authoring environment?

Live example on Binderhub

For more examples, see the original Jigsaw demo video playlist.

For creating instructional materials, we should be able to embed multiple steps of a programme in separate cells, hiding the code input cell (that is, the %jigsaw line) and then export or print off the notebook view.

LaTeX Scratch Package

The LaTeX Scratch package provides a way of embedding Blockly style blocks in a document through simple LaTeX script.

Using a suitable magic we can easily add scripts to the document (the code itself could be hidden using the notebook Hide Code Cell Input extension.

(Once again, the code cell input (the cell that contains the lines of LaTeX code) can be hidden using the notebook Hide Input Cell extension so only the rendered blocks are displayed.)

We can also create scripts in strings and then render those using line magic.

Live example on Binderhub

One thing that might be quite interesting is a parser that can take the XML generated from the Jigsaw extension and generate LaTeX script from it, as well as generating a Jigsaw XML file from the LaTeX script?

Historical Context

The Scratch rebuild – OU Build – used in the OU’s new level 1 introductory computing course is a cross platform, Adobe Air application. I’d originally argued that if the earlier taken decision to use a blocks style environment was irreversible, the browser based BlockPy (review and code) application might be a more interesting choice: the application was browser based, allowed users to toggle between blocks and Python code views, displayed Python errors messages in a simplified form, and used a data analysis, rather than animation, context, which meant we could also start to develop data handling skills.

BlockPy

One argument levelled against adopting BlockPy was that it looked to be a one man band in terms of support, rather than the established Scratch community. I’m not sure how much we benefit from, or are benefit to, the Scratch community though? If OU Build is a fork,  we may or may not be able to benefit from any future support updates to the Scratch codebase directly. I don’t think we commit back?

If the inability to render animations had also been a blocker, adding an animation canvas as well as the charting canvas would have been a possibility? (My actual preference was that we should do a bigger project and look to turn BlockPy into a Jupyter client.)

Another approach that is perhaps more interesting from a “killing two birds with one stone” perspective is to teach elementary programming and machine learning principles at the same time. For example, using something like Dale Lane’s excellent Scratch driven Machine Learning for Kids resources.

PS the context coda is not intended to upset, besmirch or provoke anyone involved with OUBuild. It’s self-contempt / self-critical, directed at myself for not managing to engage/advocate my position/vision in a more articulate or compelling way.

BlockPy – Introductory Python Programming Blockly Environment

Whilst looking around to see what sorts of graphical editors there are out there for teaching introductory python programming, I ran a search for blockly python. If you haven’t come across Blockly before, it’s a library for building browser based graphical programming interfaces, based on interlocking blocks, with a Scratch style aesthetic: blockly.

I already knew that Blockly could be customised to generate Python code but the BlockPy environment from Virginia Tech’s Software Innovations Lab is even richer:

BlockPy

For a start, the environment is set up for working with small data sets, and can display small tabular datasets as well as plot them. (You may remember we also used data to motivate programming for the FutureLearn Learn To Code (a line at a time) course.) The language is a subset of Python 2.7 (the environment uses the Skulpt client side Python interpreter; I’m not sure if the turtle demo works!).

The environment also supports blocks-to-code as well as code-to-blocks translations, so you can paste a chunk of code into the text view, and then display the blocks equivalent. (I think this is done by parsing the Python into AST and then using that as the bridge to the blocks view?)

Alternatively, it you’re happier with the blocks, you can write a programme graphically and then grab the code version. Or you can flip between the two…

BlockPy2

As well as the blocks/code view, there is a pseudo-code view that maps the code into more explanatory language. This feature is under active development, I think…

To aid debugging – and learning – the environment allows you to step through the code a line at a time, previewing the current state in the panels on the right hand side.

BlockPy3

If you get an error, an error prompt appears. This seems to be quite friendly in some cases, though I suspect not every error or warning is trapped for (I need to explore this a bit more; I can’t help thinking than an “expert” view to show the actual error message might also be useful if the environment is being used as a stepping stone to text-based Python programming.)

BlockPy4

The code is available on Github, and I made a start on putting it into a docker container until my build broke (Kitematic on my machine doesn’t seem to like Java at the moment – a known issue – which seems to be required as part of the build process)…

The environment is also wrapped up in a server side environment, and on the Virginia Tech is wrapped in a login-if-you-want-to environment. I didn’t see any benefit from logging in, though I was hoping to be able to name and save my own programmes. (I wonder if it’s also possible to serialise and encode a programme into a URL so it can be shared?)

You can also embed the environment – prepopulated with code, if required, though I’m not sure how to to that? – inline in a web page, so we could embed it in course materials, for example. Being able to hooks this into an auto-marking tool could also be interesting…

All in all, a really nice environment, and one that I think we could explore for OUr own introductory computing courses.

I also started wondering about how BlockPy might be able to work with a Jupyter server/IPython kernel, or be morphed into an IPyWidget…

In the first case, BlockPy could be used to fire up an IPython process via a Jupyter server, and handle code execution and parsing (for AST-block conversion?) that way rather then using the in-browser Python Skulpt library. Having a BlockPy front end to complement Jupyter notebooks could be quite interesting, I think?

On the widget front, I can imagine running BlockPy within a Jupyter notebook, using it to generate code that could be exported into a code cell, for example, though I’m not really clear what benefit this would provide?

So – anyone know if there is any work anywhere looking at taking the BlockPy front-end and making it a standalone Jupyter client?! :-)