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 starting 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 likely hood of success in algebra. Using this system, Edstar can identify students who are likely to be successful in advanced math. We were to work with the school to use this predictive analytics system, EVAAS, to identify participants for the program. The assistant principal of the school gave us stacks of paper printouts from Excel, of names and 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 was EVAAS, and that it was the predictions provided by last year's teachers and is what they use for math placement. We asked him to pull up this file on the computer so we could look at it. Most of the Black males were 4s. (We were kind of horrified by this.) We explained to him that this is not EVAAS, yet he insisted that it was. We had worked with this school system a lot in the past, so we had the Data and Accountability office provide the administrators in this school with their EVAAS login information. They had not ever used EVAAS. We helped them identify participants for this program. The school selected a teacher for the elective STEM course and told us she was selected because she had worked in special education. 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 class for this first year. The special education teacher they selected had no STEM skills or experience teaching rigorous courses.Which beliefs are influencing his Equity Lens? 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 his 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 They thought that providing activities and services to raise the quality of life for these students would result in more success in school. B.2 Expert vs. Evidence NA 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. 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 NA
