Data-driven, data-based, data, data, data…. Schools are consistently pushed to keep their eyes on the data. For some, it has become an exemplar in education and for others a headache! Many schools have moved towards creating data rooms.
The concept of a data room is linked with the idea of a “war room” from the military and then later adapted to business and communication. Education has embraced this concept as a way of keeping school and student data visible and central to school decision making.
Data rooms are spaces where data is displayed in a visual form. Some schools choose to use graphic representations of data; others use white board charts to display student level data. Some are elaborate; some are simple. Some include only basic information such as grades, testing and attendance, while others incorporate after-school activities, incidents and extra-curricular involvements. Presumably, once the data is displayed it is used by teachers and administrators to improve student outcomes.
If you are considering a data room, here are a few things to remember:
1. Using artificial intelligence (AI) can be a game changer or a game over.
Educators have embraced AI and why shouldn’t they – it removes those time-consuming tasks, allowing our attention to be placed on more important things. One of those time-consuming tasks is data analysis. Most educators never had time for data analysis in the first place. It’s what you do with the trend lines, those student quotes, or the test scores that matter. But be cautious. Never feed identifiable datasets into an open-sources AI interface. By doing so, that student’s private information has now been shared into the public domain, thus violating the Family Educational Rights and Privacy Act (FERPA).
2. Adhere to FERPA.
This should not need said but sometimes we forget how easily FERPA guidelines can be violated. Students and their families have privacy rights that are protected by federal law. Be sure that any steps you take to make data more available also insures that rights are protected. Be especially certain about your special needs students and the manner in which their records are handled. Data rooms cannot be spaces that are used by students, parents or community volunteers. For more information see the FERPA Guidance at USDE.
3. Use it or lose it.
Data rooms, data notebooks, data teams and other data collection tools are only useful if the data is actually reviewed and then linked to student learning. Data that is on permanent display without updates or reflection will do nothing to improve learning. Use your data room – or find another way to engage teachers with their data.
4. Maintain forward motion.
Too often I have seen endless discussions of data that focuses on only that – data. In strong student-centered environments, we want to keep the focus on the learner. Review student data with the intent of making decisions about the education of students.
5. Keep it personal.
If we are focusing on teaching and learning to improve student outcomes, then every data point examined represents a child. Data can be an opportunity to get to know a student and improve their experience of school as well as their academic outcomes. We can engage students in their own learning and goal setting.
6. Useful is more important than aesthetics.
A data display is designed for reflection, review and results – not to be a gallery. The primary criteria for the display in the data room is “Does this encourage discussion?”
7. Balance the effort to create and maintain a data room with the benefit that comes from it.
There are literally thousands of data points that can be posted and reviewed – but the time and resources to post, update and document large volumes of data can be overwhelming. Crushing volumes of data are no more valuable than a few carefully selected data points. Ask a good question – then collect the data necessary to answer the question. Time and resources are precious in schools – we want to use them wisely.
Future-ready Considerations
- As learning becomes more personalized, and in some cases moves beyond the classroom and increasingly digitally mediated, what new sources of data might be useful to display and make sense of in the data room?
- When using AI, what platforms are the right tool for the task? Many data systems already us machine learning and generative AI has been shown to not be great for quantitative data.
- Are you sure about your data analysis? If using generative AI, use as many platforms as possible and check them against each other! Be sure you know when the AI is hallucinating or leaning into certain biases.
This was written by former Director of Continuous Improvement Drake Bryan.