Over the last decade, when the “early warning system” indicators were introduced in schools to predict dropout rates, the model asked educators to analyze data from attendance, behavior, and course performance metrics. In some cases, schools had these metrics in separate systems and were able to stitch together the three indicators to identify and support their at-risk students.
Because the “early warning system” for education has proven effective, schools now regularly track and analyze these metrics. These are concrete, measurable data points that can help students stay on track to graduation. Educators and administrators know exactly which metrics to track—and how to apply that information—in order to support a specific outcome.
But what about new, emerging models of support that are based on data?
With rapid technology advances and the use of data-driven education, I get the sense that we might have just scratched the surface on how educators can use data to help students. What new applied learning sciences might be developed in the next 5, 10, 20 years by researchers and educators? How will we know what information will be most helpful to support educators next year?
We don’t yet know the answer to that question. But we do know that, when the time comes to use data to support new models in education, we need to be prepared.
Which gets me thinking about how most data scientists are approaching the unknown requirements for data? The truest analogy that I could come up with is the concept of FOMO: the Fear Of Missing Out. FOMO, which is prevalent on social media, is the idea that if you miss a party or event, you’ll miss out on something great that won’t be available ever again.
I suspect that our industry might have a bit of FOMO too—Data FOMO. We aren’t exactly sure when or how various data points might be used but we better save it just in case.
Generally, it’s better to capture information now before it disappears into a spreadsheet buried in a server, never to be seen again. We might not know what particular instructional questions we are trying to solve, but the fear of missing something drives us to capture and save it anyway. And maybe that’s ok for now. As educational technology and the ability to use data effectively and safely to support important educational outcomes evolves, having a comprehensive approach is insurance that can protect us from what we don’t yet know.
This is the type of conversation that the Ed-Fi community is having around the country. While this is simply my observation of what is happening in the field, I’m interested in what the Ed-Fi Community thinks about this issue. One thing that is certain, we are in the midst of an evolution, and Ed-Fi will continue to work with our community and partners as we all find the right path forward.
