Edstar was hired to help a non-profit that promoted equity and opportunities for Black males. The non-profit had started a program where beginning in 6th grade, the students would be mentored, they would enroll in a rigorous STEM elective to gain skills needed for success in advanced courses, and they would enroll in the advanced math track.

The state provides schools with access to a predictive analytics system, and this can be used to predict the likelihood of success in algebra. Using this system, Edstar can identify 6th-grade students who are likely to be successful on the advanced math track. We were hired to work with the school to use this predictive analytics system, EVAAS, to identify Black males with the highest predictions for success. We had met with the Superintendent of the school system, together with the President of the non-profit, and the school system was eager for this program to increase access to advanced courses for minority students.

We met with the assistant principal at the school to review the EVAAS data with him. When Edstar staff arrived, he gave us stacks of paper printouts from Excel and told us the data on this paper was EVAAS. The papers contained names, demographic data, and a column that contained a number of 1, 2, 3, or 4. The key at the top said 1 = perfect angel, 4 = OMG! and 2 and 3 were somewhere in between. He told us this variable was EVAAS, and that it was the predictions provided by last year's teachers. He said this is what they use for math placement. We asked him to pull up this file on the computer so we could look at it. Filtering the data, we saw that most of the Black males were 4s.

We explained to him that this is not EVAAS, yet he insisted that it was. He explained to us that EVAAS means data that predicts how students will do in each math track, and that is what this data does. Therefore, it is EVAAS data.

We had worked with this school system a lot, and had a good relationship with the Data and Accountability office. We worked with them to provide the administrators in this school with their EVAAS login information. They had not ever used EVAAS. They thought these teacher predictions of student behavior were EVAAS. We helped them learn to use EVAAS to identify participants for this program.

Although the non-profit running this program wanted only Black males in the program, we showed him how many Black females and Hispanic sixth graders had very high predictions for success in advanced math. He allowed us to include some of these other very academically strong students in the program, as long as the majority of participants were Black males.

Upon seeing the demographic makeup of the students selected for the program, the school selected a special education teacher for the elective STEM course the students would be enrolled in. We were not able to convince them that these students did not need special education and remedial work.

Edstar staff, Drs. Lee Stiff and Janet Johnson, together with some other volunteers, came in and taught the elective class for this first year. The special education teacher they selected had no STEM skills or experience teaching rigorous courses. We taught the students to code, using Scratch. Dr. Stiff observed their math classes, and then did enrichment activities in their elective to support what they were doing in their advanced math classes. We created Khan Academy accounts for all of the students, and Dr. Johnson monitored their work in there and gave them feedback.

We helped the students enter math and language arts competitions.