Dropout Prevention A school system hired Edstar to evaluate all of their dropout prevention programs in the district. They were wanting to move toward using data-driven decisions rather than professional judgement, and they wanted to know which of their programs were most effective. This was a medium to large district in an urban area. There were a lot of high schools. We got data on everyone who was served by their dropout programs over the previous few years. We had them articulate for us how students were identified to be in these programs, and what the objectives of the programs were. They reported that students were referred to the programs by teachers and school counselors for being “at-risk,” but “having potential.” They had not ever articulated objectives for the individuals served. They just had the general goal of the dropout rate lowering. We asked them for something measurable that they were hoping to change in the students served. They told us that these students were "at-risk" or their teachers and school counselors would not have referred them. Therefore, all of the students were below grade level on standardized tests, had poor reading skills, and were behavior problems–either with suspensions or attendance. They said their objectives were that these students would pass algebra, and English 1, both of which were required for graduation, and that suspensions for these students would be fewer and attendance would improve. We created a data file for the students served over the past several years. We found that the majority of the students were low-income or minority. About 85% of them had always scored at or above grade level proficiency on both math and Reading tests and had never failed a core course. They also had not been suspended and did not have attendance problems. (This is why they seemed to have so much potential, even though they were viewed as "at risk.")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 wanted to know which, if any, of their dropout prevention efforts were associated with the students becoming successful in school. They were attempting to move away from the perspective that at-risk students simply need services. B.2 Expert vs. Evidence NA B3. What At-Risk Means They left it to each teacher and school counselor to use their own definition of at risk, but most students referred were low-income or minority students who were successful in school prior to being referred to the dropout prevention programs. B.4 Desired Outcomes and Goals Their desired outcomes were for students to become something they already were: successful in school. They did not realize that they needed to know where students were before they set an objective of achieving something the student had already achieved. We always wonder the logic of thinking that students can be identified as needing a service by their race or income level, with the goal of achieving an academic benchmark. Yet the student is still the same race and income after the service. B.5 What is STEM and Why We Need to Fill STEM Pipeline Even though these dropout prevention programs did not have anything to do with STEM, diverting these very large numbers of high achieving students away from rigorous courses affects the STEM pipeline. SkillsS.1 Knowing What Can Be Known Educators often have so little access to easy-to-use data reports that they don't realize that they could generate a list of students who are failing core courses, or have behavior problems that will keep them from graduating on time. S.2 How to Identify Kids to Align Services By using referrals for students believed to be at-risk in general does not inform the staff about what kind of help the students needed. Most of the programs were designed to raise the quality of life experiences rather than address a specific academic or behavior need. S.3 How to Classify Things NA S.4 Skill Set Required for Working With Data They did not think they needed to have skills for working with data, because students were referred by professional judgement and services were for raising quality of life. No data or data skills is needed in this framework. S.5 Understanding Data Details NA S.6 Understanding Federal DATA-Handling Laws NA
