A school hired us to help them with their School Improvement Plan. They had provided a Poverty Framework professional development for all of the staff in which they learned that low-income kids are not as successful in school as not-low-income kids for several reasons, including their spiritual beliefs, the types of relationships they have, and their speech register, among other things. Each staff member identified a poor student and then mentored them to change them to be more like non-poor students in these areas. They surveyed the staff to have them document how they were mentoring the students, and what progress they were making in these areas. They weren’t sure what to do with the data from the surveys, or how to measure success and we came in in the middle of this project to help them.
We took the roster of students they were serving and matched them to their academic data. Nearly all of the students had already been successful before this project. The school had very few minorities in it, but about 3/4 of the students being mentored were minorities. The staff reported to us that because they did not have the lunch-status list, they “guessed” who was poor, using things like who rode the bus with the most Black students on it, speech register, and clothes.
Beliefs and Skills of the Staff
Cause and Effect: They thought that what they learned in the poverty training explained why poor kids can’t learn, so they wanted to change those things.
Knowing What Can Be Known: They didn’t seem to know that they could know which students are not successful in school by running a list using a computer, of kids who were below grade level.
Identifying Kids to Align Services: They thought that they could use information like what bus the kids ride, or their clothes to determine who needed their services, which they believed were for poor kids.
Skills for Working With Data: Although we did not use their surveys about how they were changing the students, doing so would have been very difficult because they were on paper, open-ended when they could have been forced choice, and generally not usable data.
The kids served were already successful in school before service. Some of them were recommended for remedial services and tracked lower in math because the staff believed they were at risk of failure even though they were successful.
The second year that we worked with this school, they learned to use academic data to align academic support services for students who were not successful. They also learned that successful minority students benefited from placement in rigorous and enriched courses.