The vast field of education comes with unique set of challenges from understanding complex policies to serving students with differing socioeconomic backgrounds. Rigorous research is often done to equip teachers with effective pedagogy and inform policymakers of improved solutions. However, these long-detailed reports don’t often have the impact on policy they should because their complexity is hard to translate.
This is where effective data visualization comes to the rescue. By making the data digestible, interactive, and solution-oriented through rigorous UX, we are lessening the burden on educators and policy makers to sift through data and empowering them to make informed choices for their next steps.
And that is exactly what we did with the amazing team at Center for Research on Education Outcomes at Stanford University (CREDO). By applying UX methodology and concepts, we were able to translate rigorous research into something that could be easily understood by educators, policy makers, and education leaders. For the first time in history, this type of interactive report was also accepted by the U.S. Department of Education as an official submission for evaluation.
Building this type of visual report took more than just design skills though. Our ProjectEd Education Data Storytelling Design Model consists of these key steps:
1. Look at the research as a whole before diving into the nitty gritty.
It’s important for the team to look at all the data, not just the pieces that we presume are important. The team works together with data scientists and researchers to fully understand the research from initial theories of action, surprises along the way and final results.
We worked together with CREDO to conduct an exhaustive review of all of their research. By doing so, we were able to identify together the key components of the data that needed to be translated to the target audience (i.e. educators and policy makers) and ensure that it tied back to the research as a whole.
2. Carve out a coherent path by asking the right questions.
In the same way that we must take a step back to see the big picture, we must also “zoom in” to see the intricate details and moving parts that make the picture whole. That’s why it’s important to ask certain questions before rushing to the blank canvas:
Who is looking at this data?
Where will they be accessing this information?
Who needs to know what information?
In order to do this, we must also ask and research what each stakeholder needs to learn from the data such that each view while different will be equally effective.
3. Design, Build, Test, Repeat
Once we fully understand the data, the target audiences and the impact it needs to have, then we design and build the best possible data visualization. Depending on the goals, these can be websites, animations, infographics, or more traditional downloadable reports.
In all cases, testing the results with real users is key. This extensive and careful research is done because we want to make education better and to do that we need our data to be understood and have an impact with those that need to understand it to make informed decisions.
Effective data visualization is no easy feat, but the end results are impactful. This intersection between UX design and rigorous research creates another tool that educators and policy makers can use to move education forward.
Want to learn more about data visualization and how it could potentially benefit your edtech research? We're happy to answer any of your questions. Feel free to send us a message.