16.09.2015 by Evangelos Kapros
In Dublin, Ireland, Evangelos Kapros works with the Learnovate Centre— an Educational Technology research centre in Trinity College, the University in Dublin. One of the areas he is working on is Data Visualization. “It’s a great area to work on for education for a number of reasons,” says Evangelos. These reasons involve both using data visualizations to teach subjects like History, Statistics, or Journalism, and teaching Data Visualization as a subject itself. Evangelos hereby shares his thoughts about the latest trends he observes in data visualization for education.
One reason why Data Viz is a great area is that it truly increases the level of data literacy in a number of ways. The first thing that comes up with data literacy is something that many learners do. When they first start to do any type of data visualization they have a pre-conceived idea of what they want to do. It’s pre-conceived with opinions sometimes or other biases, ideologies or any other type of preconceptions. And they want to put it into an image. So they start very slowly to understand that this is not how data visualization works. You have to work with your data and the conclusion has to naturally emerge from that. You can not show whatever you want, but you should show what your data actually tell you. It’s one thing. It’s a way that gives more objectivity in the curriculum. Not just for the obvious things: to teach statistics you will need some type of visualization because it will make something a lot easier to understand. It may also help to eliminate some biases. When I talk about education I sometimes find that many make an assumption that early (K-12) education is implied; however, sometimes in mature students or continuous education biases may be more difficult to eliminate as they may have been established in a person for a much longer time.
Additionally, data visualization can teach principles of data literacy regardless if it is being taught as a subject or used to teach other subjects. For instance, the principle of GIGO (garbage in, garbage out) is implicitly being understood by anyone who fail to get their visualization right the first time and they have to iterate.
Incidentally, it’s not enough to just show or create visualizations. Again, another example—many curricula offer artificial exercises for data visualization. They get students to count the height of their family members and make a histogram or something like that. It’s something that it’s not really interesting for students necessarily. If they had an exercise that was around football or baseball or something that they are actually interested in, it would be a lot more exciting.
Motivating students is of paramount importance in a curriculum and cannot be overestimated. Visuals can be a stimulating experience for many students, and they should be used in a way that supports and is supported by learning design; for all our knowledge in Data Visualization, educational principles of teaching and learning are not optional. For what they’re worth, data visualizations are not automagically going to make someone a better educator.
Secondly, there is increased level of visual literacy nowadays and it’s a thing that goes both ways. By teaching Data Visualization, you increase data literacy and that leads to increased visual literacy. It also goes the other way around. Because there is a lot of already existing media that’s visual it also leads to data literacy. So it goes both ways. You understand data better because you are familiar with visuals and you understand visuals better because you get familiarized with data, what you can do with them and how you can work with that. So that’s a very exciting thing and it’s very exciting to be in an occasion where you can share so many data visualizations on the web and see what happens and have tools available that make it possible like Infogram, for example. Again, this circle of data/visual literacies functions in both cases when Data Visualization is taught as a subject or used to teach another subject.
Visualization Tools and Usability
Another thing that I want to talk about is tools that enable this happen. One thing is how we think about visualizations in education, how they work and how they are different from traditional teaching. One way they are different is that sometimes they need more technology than other means. That depends a lot on the curriculum, how it’s organized, if you have books available, and if you have a classroom that you can customize. If they let you put stuff on the wall, you can improvise. You can make visualizations with stacking boxes on top of each other. Then you don’t need that much of technology.
Oftentimes, however, you do need technology. Still, contemporary digital technology has only gone so far that we rarely see visualizations of data sources that are easily pluggable into each other for educational purposes. We see many APIs that one can argue they are built more for the convenience of their creators rather than for the convenience of the consumers (developers/tech savvy educators).
One thing that we are looking here in Learnovate is the usability of educational APIs. When most people talk about usability of data visualizations, they automatically think about the visual aspect of it, but also APIs and how data plug in tools is a huge part of usability of data visualizations. So a big question that this entails is how we are going to have technologies that are going to be robust and fast so that we can show to students what we mean when we want to visualize x, y, z.
This aspect sometimes surprises people. Usability in something that is not something visual, but it’s still there, it’s still a part of the EdTech ecosystem and I think there is much work that can be done in this area.
I would be more than glad to learn what others think about this, do not hesitate to drop me a line or two!
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