Digital Data Stories
Welcome back to the Digital Literacy Program (DLP) here at the Alexandria Archive institute! Currently, we’re wrangling the evaluations of our first Data Story. If you haven’t herd about it, mosey on over here to find out more. In the meantime, we thought we’d share our recipe for a good tutorial.
While we can’t give an ingredients list, we can share some guiding principles. Besides deciding how to test the efficacy of our work, we considered how our data stories were different from existing tutorials. But we also didn’t want to start from scratch.
For this, we reflected on our own experiences and existing projects like The Programming Historian and The Open Digital Archaeology Textbook. We thought about tutorials we had used before. And if there were any we kept coming back to. After considering those, we went through a lot of options before starting our first data story.
However! We don’t have to dive into all those options, because we’ve considered the steps we took and reconstructed them into a set of questions. The first question was the biggest:
What goes into our tutorial?
While that question introduces many others, it comes down to a more manageable question of:
What do we want to teach?
Although that is also a huge question, it was something the DLP had already decided. We wanted to teach archaeological data literacy. This will be different for other projects but once that’s decided, it’s a lot easier to answer the next questions.
For us the next question was:
What assumptions will we make in this tutorial?
While it can be terrible to assume things, we have to start off with a few assumptions or else we can’t start making a tutorial. These assumptions allow us to decide which concept a tutorial should start with. After chewing the code for Cow-Culating Your Data With Spreadsheets And R, one may have noticed that we assume users have only a basic familiarity with spreadsheets and R.
These are some of the concepts that were part of the first data story. These steps help scaffold the basic skills we need to know before manipulating data
Furthermore, considering assumptions leads well into our next question, which was:
Who do we want using the tutorial?
Who we want using a tutorial may also drive our assumptions about where to begin. For our project, we defined “who” as students of- and life-long learners in- archaeology. Once we figure this out, it’s much easier to address other questions like:
What sort of language should we use?
How long should the tutorial be?
Those questions are important when we consider the intended audience, what assumptions we make in the tutorial, and depend on what we want our audience to consume in a single sitting. Our last question and possibly the most important is:
How do we get folks to use our tutorial?
When we write tutorials for a class, we have a guaranteed audience. However, for tutorials that are public, like data stories, it’s difficult to know how to get folks to use it.
We chose to do this by collaborating with specific communities. Our primary method was by asking educators if they’d be interested in the tutorials. Then we asked how they wanted to incorporate them into their classes.
We hope that by keeping these questions in mind as we write more data stories, they will help this program to build a successful series that improves archaeological data literacy.
