Due Date: before Tuesday, February 3rd (reading and initial posting)
Turn-in link: Discussion on Canvas
Hopefully, you’ve realized that being able to talk about tasks and data is important to being able to talk about visualization. Talking about data is generally easy, talking about task is harder.
To get us started in this, we’ll see what Munzner has to say on the subject. So, for this assignment, read:
Data is easy – Chapter 2 is straightforward. There may be different choices in names, but that’s just the details.
There are many ways to think about tasks abstractly. I haven’t seen one yet that totally nails it. Munzner’s (which actually comes from a longer paper where they have an even more complete model) is about as good as I’ve seen so far. But view it as a structure for thinking about task, not the definitive way to do it. There will be a future assignment where we look at some other ways to think about task abstraction.
For the discussion:
Identify some concrete tasks that you might want to do with data, and then try to figure out what the more abstract task is, and where it fits into Munzner’s taxonomy. Find tasks for at least 3 of the “action” categories (the things on the left side of Figure 3.1). Don’t use an example I’ve discussed in class, or that appears in the book.
For example: In class, I showed the “concrete” problem of trying to identify students who were hurt by rounding error. A more abstract problem is trying to find boundary cases. In Munzner’s taxonomy, this might be an “Identify” Query task. (although, there are some other categories you might argue it falls into)
As a bonus challenge: try to come up with data tasks that don’t fall nicely into Munzner’s taxonomy, or are really ambiguous as to where they would fall.
In case you missed it (grading):
We’ve updated the explanation for the grading scheme (what it means to get a 4 vs. a 5, etc.) and our objectives for the discussions. If you have any questions, please feel free to contact the course staff!
