STEM Scholars 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.Which beliefs are influencing the Equity Lens this school was operating under? Click to check your answer. B.1 Cause and Effect B.2 Expert vs. Evidence B.3 What At-Risk Means B.4 Desired Outcomes and Goals B.5 What is STEM and Why We Need to Fill STEM Pipeline Which skills are influencing the Equity Lens? Click to check your answer. S.1 Knowing What Can Be Known S.2 How to Identify Kids to Align Services S.3 How to Classify Things S.4 Working With Data S.5 Understanding Data Details S.6 Understanding Federal Data-Handling Laws BeliefsB.1 Cause and Effect The school administrators and the STEM elective teacher did not understand that rigorous enriched activities would help these high achieving students succeed in the advanced track. B.2 Expert vs. Evidence They used teachers' prediction of future behavior for math placement. They had never logged into their EVAAS accounts before this, although it had been available for years. B3. What At-Risk Means They thought a class that was primarily Black males would need an experienced special education teacher and remedial work, even though the whole point of the program was to identify high achieving successful Black males and provide them with rigor. B.4 Desired Outcomes and Goals They could not get past thinking this was to be a program for at-risk students. They did not understand that the goal was to put these students on track for success in STEM courses. B.5 What is STEM and Why We Need to Fill STEM Pipeline Most of the middle schools, including those in this program, have a STEM elective, taught by someone without a strong math, science, or technology background. They do creative activities in the STEM courses. The next year, this program was expanded to another middle school and served only Black males. The teacher for their STEM elective had no background in math, science, or computers. They did craft-like activities in the course. SkillsS.1 Knowing What Can Be Known They did not know that you could print lists of students who were likely to be successful in advanced math courses. S.2 How to Identify Kids to Align Services They used teacher judgement of the behavior as a prediction for math track placement. Because they put it into a spreadsheet, it had the feel of valid data. The assistant principal actually thought this was EVAAS because he had heard EVAAS described as predictive data. S.3 How to Classify Things NA S.4 Skill Set Required for Working With Data They had no skills for working with data. They printed the Excel files and used the paper rosters to do their course placement because they did not know how to use any features of Excel (e.g., sort, filter, etc.). To them, the electronic version was just like the paper version, only on a screen. They had pasted the headings repeatedly throughout the spreadsheet so they would print on every page. S.5 Understanding Data Details No understanding what so ever. They did not know the difference between an opinion about behavior and a prediction based on math scores. S.6 Understanding Federal DATA-Handling Laws The school staff did not know anything about FERPA laws. They left these Excel spreadsheets of what they believed to be EVAAS data sitting on tables in the library.