Teaching Digital Technologies

by Rob Poulter


The Australian Curriculum has been going through the "everything is changing" part of the ten-yearly cycle recently. As a teacher of technologies[1] it's been both exciting (and gut-wrenching) to see how the draft curriculum has evolved to the point where it is required for implementation in 2018.

I won't go into the design of the digital technologies curriculum itself (you can see the details at the ACARA site) but did want to look at some of the ways people have been talking about implementing the mandatory section (up to and including year 8).

As anyone who is familiar with the Internet and nerds would know, people love arguing about programming languages and platforms. Since I started teaching Computer Science as a year 11/12 course (then called Information Systems) in 2005 I remember reading arguments over what programming languages and databases to use, how to assess them, and so on. Fast forward a decade or so, and sit down with a couple of teachers, and you can have the same argument about how to teach programming (or more specifically 'algorithmic thinking'), this time to students 5-6 years younger.

In the fine tradition of cats on the web.

In the fine tradition of cats on the web.

The Web

As an example, I was recently at a moderation session for a year 11/12 course and got to talking to a couple of other teachers there about how they were appoaching Digital Technologies for younger kids. One of the teachers was talking about website design, and learning HTML/CSS/JS. This bothered me on a couple of levels. Firstly, I've never had a lot of success with doing much that's useful with the fundamentals of web design in the classroom. There are usually a few students that really run with it, but there's often a lot of frustration at HTML elements not working as expected (which I'm sure would be shared by anyone who has worked with the web before) as well as with juggling content, presentation and the mish-mash of adding programming into the mix. Secondly, I wonder what the point is. Content creation has largely moved away from requiring an understanding of the underlying technology, we have a bunch of free or cheap platforms to build on like WordPress, SquareSpace, Wix, Weebly and so on, and other tiers as you get further toward the HTML/CSS stage like the various Markdown converters (which admittedly get closer to needing to care about styling for templates).

If we look at understanding for web usage in business, then unless students are going into web development, then again they'll probably be looking at content management systems with integrated editors rather than needing to know (or have much advantage from knowing about) the nuts and bolts.

Arduino. Credit Wikipedia

Arduino. Credit Wikipedia

Hardware

With the Maker movement in full swing in schools there has been a lot of enthusiasm behind the various microprocessor platforms like Arduino. I've had a conflicted relationship with Arduino with my students since I find it difficult to bridge the gap between electronics and programming, and since programs are compiled and then run on device, programming is usually done in C++, which I think is a very unfriendly learning language, partly due to syntax and partly due to the typically obscure debugging information that you get from C style languages. Earlier this year I was shown ScratchX, which can allow you to plug the lovely Scratch interface into other stuff, like Arduino and its variants (my favourite one so far is Hummingbird). The trouble is that this goes from on-device execution to running code on an attached computer, meaning you're stuck trailing wires around. At a recent conference I was found out about ArduBlock and Visuino, both of which are visual, block-style programming environments but which compile down to Arduino binaries which can then be run on-device. I think these styles of programming interfaces are going to go into my toolbox for my guinea pig classes to test suitability in the near future.

Scratch Logo. Credit: Scratch.mit.edu

Scratch Logo. Credit: Scratch.mit.edu

Scratch

Speaking of Scratch, at the 2016 ECAWA conference I had the pleasure of going to a session by Brett Clarke and Mark Stephens (I tried to find something that wasn't LinkedIn, honest), which looked at adapting materials which Brett wrote for the computing curriculum back in the early 1990s. The session looked at using turtle graphics style commands built as Scratch extension blocks to build complex shapes from primitives, which then extended to scaling and the introduction of parameters to extensions. It was a really nice, accessible way of looking at problem decomposition and encouraging experimentation in an environment which is very fault-tolerant (that is, no syntax errors, colour categorisation of code blocks, and immediate visual feedback).

They went on to look at extending the same principles to build a series of methods for drawing letters (essentially a vector font), straight, curved and mirrored text, and then looking at how this could be applied to lettering on a sewing machine with a zig-zag pattern.

My Classroom

I've been looking at different vectors for teaching parts of the new curriculum this year with my Year 7 and 8 students (neither group really has the time available to cover the lot yet). I've been having a fair bit of success with Scratch, and I'll probably incorporate some of the ideas which were covered in the session with Brett and Mark. I'd like to add a bit more visual design into the mix, which allows some investigation into vector and raster image manipulation, but I'm not sure how much time I'll have. Scratch has an inbuilt editor which allows for both vector and raster tools, but it isn't particularly powerful and I'd like to use something that is a bit more flexible.

I am pretty excited to look at the visual Arduino interfaces. I think there's so much potential for hardware hacking and a huge number of startups using them for prototyping of real world projects which students can be pointed to for inspiration and motivation. One of my projects is to put together a program where I can devote a significant chunk of time to using Arduino in the classroom to address a nice wide swathe of the Digital Technologies curriculum descriptors.

[1] - Side story, when I was enrolling in my teaching diploma I was looking for majors which fit in with my Computer Science background. Only one university offered a computing major, so I signed up for it, with "design and technology" being my second preference. There was no description about what this "design and technology" entailed but I figured it had technology in the name, so would be fine.

Only later did I find out that "design and technology" was the catch-all for woodwork, metalwork, and so on; areas that when I started in schools were definitely not technology-focused, although in the era of cheaper 3D printing, CNC routers and so on this is becoming less of the case.


Institutional blocking as a service

by Rob Poulter


I teach a lot of different students this year - the entire year 9 cohort, most of the year 7s and 8s (spread over the year), and a few classes of year 10s. Seeing all of them regularly gives me a pretty good look at how they use technology (with the exception of mobile since, like many schools, students are prohibited from using their phones during the day), which makes for some interesting conversations.

During last term I was thinking about streaming music services, partly because my Apple Music trial had run out and I was thinking about whether I listened to enough music to pay for it, and partly because students are usually pretty sneaky about getting what they want and so if their regular streaming sites are blocked (like YouTube or Spotify) they'll keep looking until they find a replacement. This got me to thinking about whether the blocking of services by institutions like schools, universities and workplaces could contribute to the growth of companies that for some reason do not fall into the blocking black hole. Additionally, is that usage sticky enough that it could persist outside of the institution and translate into long term support for a service.

I figured music would be a good avenue to pursue here since there are so many streaming services, and all but a couple are blocked on the school network. The service which I focused on was SoundCloud, since it seems to be the one that is most popular with students when they can get away with it.

Since I have so many guinea pigs to ask about this sort of thing, I put together a quick survey to ask my year 9s about how they consumed music. The questions I put together were:

  • Do you regularly listen to music?
    Options for what sort of device is used (desktop/laptop, mobile device, non-cellular portable)
  • If you listen to music at school, what method do you use?
    Options for downloaded files, streaming music, streaming video, no use
  • If you listen to music at home, what method do you use?
  • Again, downloaded files, streaming music, streaming video, no use
  • Select which streaming music services you have used
    Options for all the main services I could find in Australia, plus an 'other' option
  • Which streaming music service do you use most at home?
    Options for the previous list plus not listening at home
  • What is the main reason for using this service at home?
  • Repeat of the last two questions in a school context

I got about 50 responses back, which isn't bad considering I forgot to send the survey link to two of my classes since when I decided to do this I was also hounding them to get a project handed in and I was a little distracted in class.

The first thing I noticed once I started getting a significant number of responses back is that students don't read questions (although to be honest, I learned that one in my first year of teaching). When asked to choose streaming music services (specifically mentioning this was audio and no video in the question), there were a significant number of responses which were streaming video 🙄.

Overall Usage

Unsurprisingly, the majority of students said that they mostly listened to music on their mobile phones. 82% of respondents used their mobile, 18% a non-cellular mobile device, and 12% on a laptop or tablet.

School Usage

Of the students who listened to music at school, 57% of them used some sort of streaming service, 27% used downloaded media, and 16% used a video streaming service.

Home Usage

Of the students who listened to music at home, 47% of them used some sort of music streaming service, 38% used downloaded media, and 15% used a video streaming service.

Music Consumption Methods

Music Consumption Methods

Streaming Services

When it comes to services which are being used, there was a surprising spread of services out there, although the fact that Apple Music and Spotify were the highest used services wasn't too surprising. I suspect that the Apple Music figure is a bit inflated since I think some students might have (quite understandably) mistaken music being played throught he Music app with music being streamed.

Music Services Used

Music Services Used

When looking at the most used music streaming services at home and at school in the chart below, there's the bit of data which I'm mostly interested in. Eight respondents used SoundCloud in preference to other services at home. I'll have to run another survey or two in the future to see how this changes.

Music Services Most Used

Music Services Most Used

Most of this is pretty hand-wavey. Without looking at a wider range of ages at school and doing some longitudinal study to see how sticky some of these services are (which is made more difficult by the volatility of online business), there isn't much point speculating as to what it means.

Anyway, just something for the giggles.


Machine Learning and Intellectual Laziness

by Rob Poulter


So there's a certain element of hyperbole coming up. Just saying.

Google Allo (which of course makes me think of the classically cheesy 'Allo 'Allo) was outlined in the recent Google IO keynote. One of the features which bears some consideration is Smart Reply which suggests replies to messaging and learns from your responses over time (and I assume also does learning on the aggregate, playing to Google's strengths in large scale data analysis.

Over at the Google blog they have this to say about Smart Reply

Smart Reply learns over time and will show suggestions that are in your style. For example, it will learn whether you’re more of a “haha” vs. “lol” kind of person. The more you use Allo the more “you” the suggestions will become. Smart Reply also works with photos, providing intelligent suggestions related to the content of the photo. If your friend sends you a photo of tacos, for example, you may see Smart Reply suggestions like “yummy” or “I love tacos.”

This sort of suggested reply scheme has existed for a while. Google Inbox has done this for a while. iOS has had suggested words above the keyboard for a while now as well (although I doubt anyone uses it in the predictive sense as opposed to suggestions once you start typing, except for entertainment purposes).

One of the things I worry about is the problem we already have with the idea of filter bubbles: The content which algorithms predict we will want is preferentially shown to us based on what we have viewed, liked, tweeted about and so on.

While the idea of filter bubbles is a bit more extreme than suggested replies, the concept is pretty much the same: an algorithm suggests ideas to us which, although some would be based on how we have reacted to similar content before, Google's strength has always been analysis of aggregate data, so some aspects are always going to be taken from how other users have reacted and trained the system. As a result there will probably some homogeneity to the suggested context for the reply.

Assuming that Smart Reply gets good enough to actually use day to day, at what point do we as users start thinking of these replies as "good enough" and stop thinking about the nuance of the language we use? A parallel situation that I think already shows this sort of effect is the use of emoji in text messages, tweets and so on. We often use a "good enough" glyph in place of articulating our thoughts more thoroughly.  (Full disclosure: I adore the shocked face emoji and use it at every opportunity 😱)

So what I wonder is, if we allow machine learning algorithms to suggest responses for us, do we reach the point where we actually stop thinking about the content we view as thoroughly as we might otherwise do (or perhaps as much as we should do)?

As a relatively unimportant example, let's look at images which demonstrate perceptual ambiguity, such as the one which I'm sure everyone has seen, the old woman/young woman. In one case, we look at a picture and respond with that it's a young woman and in another an old woman, or perhaps we think about it more and see both and comment on that. On the other hand, what if Google's image analysis kicks in and suggests that it's a photo of a young woman, do we accept this suggestion and not see the old woman?

It's highly likely that Google's software has already identified something like this and is ready to provide an accurate analysis of what the image is, so what about something that is more difficult to un-see once it has been viewed in a particular way? Whilst on holiday recently my wife and I were watching afternoon TV while waiting for the car to charge and a show on optical illusions came on. Many of the examples were tricks of perspective, but the one that stuck with me was the spinning dancer, which could appear to be spinning either clockwise or anticlockwise. With the right suggestion, it was possible to perceive it as spinning either way, but once we saw it it was quite difficult to switch to the opposite direction. In this instance my wife initially saw it as spinning anticlockwise and I saw it the other way. A simple suggestion can go a long way in the way this is perceived.

Spinning Dancer Nobuyuki Kayahara CC BY-SA 3.0

Spinning Dancer

Nobuyuki Kayahara CC BY-SA 3.0

So anyway, while I've been listening and reading the opinions about chat bots everywhere (Facebook) and now Smart Reply (Google) and people and either insisting that this is The Future of eCommerce or some such, I'm left wondering whether they'll have a subtle effect on our thinking when they get to the point of actually being reliable enough to be plausibly accurate. 


Pebble Health

by Rob Poulter


I've written about my Pebble before and the things that I like about it.

This week a new firmware and app version got pushed out and I noticed that after tracking health data for some time (steps and sleep), the iOS app finally got a reporting screen for it.

I thought it was worthwhile looking at, since it has a really nice reporting graph. Rather than simply reporting the number of steps and sleep hours, it gives an area plot showing current and typical data.

This is a big improvement over the iOS Health app reporting, which shows you the daily average, but doesn't give you anywhere near the level of comparison between your typical activity and current activity.

Additionally, the health settings has an "Insights" section, which presumably gives you feedback on your habits.

Pebble Health Insights Settings

Pebble Health Insights Settings