I’m up to my neck in books on visualisation and data analysis at the moment. So up to my neck that this post is going to be pretty short – and you know how much I love to talk! I’ve spent most of the evening preparing for tomorrow’s visualising data tutorial for Grand Challenges and one of the things I was looking for was bad visualisations. I took a lot away from Mark’s worked examples posts, and I look forward to seeing the presentation, but visualisation is a particularly rich area for worked ‘bad’ examples. With code, it has to work to a degree or manifest its failure in interesting ways. A graphic can be completely finished and still fail to convey the right information.
(I’ve even thrown in some graphics that I did myself and wasn’t happy with – I’m looking forward to the feedback on those!) (Ssh, don’t tell the students.)
I had the good fortune to be given a copy of Visual Strategies (Frankel and DePace) which was designed by one of the modern heroes of design – the amazing Stefan Sagmeister. This is, without too much hyperbole, pretty much the same as being given a book on painting where Schiele had provided the layout and examples. (I’m a very big fan of Egon Schiele and Hundertwasser for that matter. I may have spent a little too much time in Austria.) The thing I like about this book is that it brings a lot of important talking and thinking points together: which questions should you ask when thinking about your graphic, how do you start, what do you do next, when do you refine, when do you stop?
Thank you, again, Metropolis Bookstore on Swanston Street in Melbourne! You had no real reason to give a stranger a book for free, except that you thought it would be useful for my students. It was, it is, and I thank you again for your generosity.
I really enjoy getting into a new area and I think that the students are enjoying it too, as the entire course is a new area for them. We had an excellent discussion of the four chapters of reading (the NSF CyberInfrastructure report on Grand Challenges), where some of it was a critique of the report itself – don’t write a report saying “community engagement and visualisation are crucial” and (a) make it hard to read, even for people inside the community or (b) make it visually difficult to read.
On the slightly less enthusiastic front, we get to the crux of the course this week – the project selection – and I’m already seeing some hesitancy. Remember that these are all very good students but some of them are not comfortable picking an area to do their analysis in. There could be any number of reasons so, one on one, I’m going to ask them why. If any of them say “Well, I could if I wanted to but…” then I will expect them to go and do it. There’s a lot of scope for feedback in the course so an early decision that doesn’t quite work out is not a death sentence, although I think that waiting for permission to leap is going to reduce the amount of ownership and enjoyment that the student feels when the work is done.
I have no time for paddling in the shallows, personally, and I wade on in. I realise, however, that this is a very challenging stance for many people, especially students, so while I would prefer people to jump in, I recognise my job as life guard in this area and I am happy to help people out.
However, these students are the Distinction/High Distinction crowd, the ones who got 95-100 on leaving secondary school and, as we thought might occur, some of them are at least slightly conditioned to seek my approval, a blessing for their project choice before they have expended any effort. Time to talk to people and work with them to help them move on to a more confident and committed stance – where that confidence is well-placed and the commitment is based on solid fact and thoughtful reasoning!
Oh, the poor students that I spoke to today. We have a new degree program starting, the Bachelor of Computer Science (Advanced), and it’s been given to me to coordinate and set up the first course: Grand Challenges in Computer Science, a first-year offering. This program (and all of its unique components) are aimed at students who have already demonstrated that they have got their academics sorted – a current GPA of 6 or higher (out of 7, that’s A equivalent or Distinctions for those who speak Australian), or an ATAR (Australian Tertiary Admission Rank) of 95+ out of 100. We identified some students who met the criteria and might want to be in the degree, and also sent out a general advertisement as some people were close and might make the criteria with a nudge.
These students know how to do their work and pass their courses. Because of this, we can assume some things and then build to a more advanced level.
Now, Nick, you might be saying, we all know that you’re (not so secretly) all about equality and accessibility. Why are you running this course that seems so… stratified?
Ah, well. Remember when I said you should probably feel sorry for them? I talked to these students about the current NSF Grand Challenges in CS, as I’ve already discussed, and pointed out that, given that the students in question had already displayed a degree of academic mastery, they could go further. In fact, they should be looking to go further. I told them that the course would be hard and that I would expect them to go further, challenge themselves and, as a reward, they’d do amazing things that they could add to their portfolios and their experience bucket.
I showed them that Cholera map and told them how smart data use saved lives. I showed them We Feel Fine and, after a slightly dud demo where everyone I clicked on had drug issues, I got them thinking about the sheer volume of data that is out there, waiting to be analysed, waiting to tell us important stories that will change the world. I pretty much asked them what they wanted to be, given that they’d already shown us what they were capable of. Did they want to go further?
There are so many things that we need, so many problems to solve, so much work to do. If I can get some good students interested in these problems early and provide a coursework system to help them to develop their solutions, then I can help them to make a difference. Do they have to? No, course entry is optional. But it’s so tempting. Small classes with a project-based assessment focus based on data visualisation: analysis, summarisation and visualisation in both static and dynamic areas. Introduction to relevant philosophy, cognitive fallacies, useful front-line analytics, and display languages like R and Processing (and maybe Julia). A chance to present to their colleagues, work with research groups, do student outreach – a chance to be creative and productive.
I, of course, will take as much of the course as I can, having worked on it with these students, and feed parts of it into outreach into schools, send other parts in different levels of our other degrees. Next year, I’ll write a brand new grand challenges course and do it all again. So this course is part of forming a new community core, a group of creative and accomplished leaders, to an extent, but it is also about making this infectious knowledge, a striving point for someone who now knows that a good mark will get them into a fascinating program. But I want all of it to be useful elsewhere, because if it’s good here, then (with enough scaffolding) it will be good elsewhere. Yes, I may have to slow it down elsewhere but that means that the work done here can help many courses in many ways.
I hope to get a good core of students and I’m really looking forward to seeing what they do. Are they up for the challenge? I guess we’ll find out at the end of second semester.
But, so you know, I think that they might be. Am I up for it?
I certainly hope so! 🙂