Using data to solve boundary challenges

Redrawing boundaries reallocates a district’s resources at a fundamental level, and with that reallocation comes a great deal of concern, anxiety, and turmoil as members of the district’s community worry whether their needs will be addressed properly. Compounding that tense situation, districts are often working with incomplete data and making what amounts to educated guesses about where resources will do the most good.

Muskogee Public (OK) Schools serves about 5,600 students across 13 school sites. Between a declining birth rate in the area that we serve and more students and their families choosing nontraditional options such as online schools, our enrollment recently shrank to the point that we needed to close a building and reallocate some of our resources.

We settled on turning one elementary school into a 6th grade center and then closing a middle school and moving its students to a larger elementary school. We’re closing only one building, but that still requires rezoning the remaining five elementary schools and the 2,700 elementary-aged students they serve.

Here’s how the strategic use of a wealth of data presented in easy-to-understand formats was key not just in informing our decisions but in making sure members of our community understood and supported those decisions.

Using data to understand needs

Leading the process was our long-range planning committee, a group of 35 community members selected by our school board, the superintendent, and prominent members of the community. To begin, the committee was operating under the assumption that our schools were neighborhood schools.

But when we began to dig in with our data software, a geographic information system (GIS) tool called ONPASS® Pro from Educational Data Systems, we found that one of our sites didn’t meet our assumption. This school is in a great neighborhood, and a lot of homes within its boundary feed into it. But the population in that part of town is aging. When we looked at the data, we found that the school had about a 65 percent intra-district transfer rate. It was hard to believe that only 80 kids attending this school lived within the actual school zone.

Related: 3 data-driven tips for successful bonds and levies

Seeing accurate and up-to-date data helped us look at things from several perspectives and understand students’ demographic attributes. The data made it clear that this was not actually a neighborhood school—we realized this was a low-income school area with more diversity than some other areas. Our software helped us create different planning scenarios that reflected both that data and the community’s input.

After studying the data, we changed our previous scenarios to be a better reflection of each area in town and provided instant feedback for our planning committee as it moved forward with the process.