To help us organize our Data Stories the Data Literacy Program (DLP) decided to organize the stories into seven series. We’ve mentioned this a few times and we thought it would be good to explain a bit more about what these series are and why we chose to do this.
As part of our commitment to literacy, we wanted to ensure that we created Data Stories that highlighted various literacies. While data literacy is its own thing, to become data literate people need to be literate in other ways first. The biggest one is alphanumeric literacy, or what we usually think of as “literacy”. This is the ability to read and understand words when displayed as visual symbols.
In addition, the ability to cultivate literacy skills, aka how we actually learn to be literate, can be done in a lot of ways. Because we tend to think that data = numbers, many data literacy resources focus on logical/mathematical learners exclusively. There is, on occasion, some space given to read/write learners because we provide instruction using text.
However, that doesn’t help the folks who don’t learn well in those fashions. In addition, archaeology (the specific kind of data literacy we’re interested in) has strong kinesthetic (movement-based), visual, and other sensory components. These qualities mean that people need to read observations that do not appear as spreadsheets.
Often, data is verbal (written in notes) but it is also generated through visual, auditory, olfactory, and touch-based observation. And to create and describe sensory observation well, we need to cultivate different kinds of skills.
These intersecting factors meant that we needed to create Data Stories that catered to the different skills that underpin archaeological data literacy. That way we teach archaeological data literacy by creating resources that allow many kinds of learners to cultivate those skills.
We decided that the best way to do this was by not just picking data sets or skills to focus on but by ensuring that we created data stories that fit various kinds of literacy learning. And that’s how we came upon the idea of utilizing the structure of different series.
This structure allows diverse learners the opportunity to pick data stories that should help them to cultivate their data literacy. We can’t capture all learning styles but we felt that data stories should be diverse. Therefore, the series we settled on are:
Based on their titles, folks can probably imagine how each might relate to data literacy. And instead of trying to summarize each now, we’ll write separate articles to explain how each cultivates particular data literacy skills in the coming months, so stay tuned!