Raising Achievement and Closing Gaps We worked with a magnet middle school that served high income and students from the housing projects. We created a data profile for the School Improvement Team to compare achievement with course enrollment. Nearly all of advanced math and English courses enrolled only high-income students, yet many of the low-income minority students were high achieving in math and reading. We also saw that although by 8th grade the achievement gap between White and minority students was large, when we looked at data from before the students entered there had hardly been a gap. This had not been evident before because the students came from more than 30 different elementary schools into this magnet school. , The principal moved into the top track over 100 low-income and minority kids, based on data. They were all successful. We have seen in many school systems that using data to identify students for enrollment into the most rigorous courses raises the schools’ achievement and closes gaps. In many other schools, we simply showed them how to use data to identify the students likely to be successful. One school increased the number of students in their most rigorous courses four fold. In every case, overall school achievement rose and gaps narrowed significantly.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 Access to the most rigorous courses caused higher achievement for the high scoring students. B.2 Expert vs. Evidence The school switched from using professional judgement to using data to identify students for advanced courses. B3. What At-Risk Means The meaning of at risk changed in the principal's equity lens, but not for many staff members. This caused conflict. It is hard to say whether the high-income parents don't want equity, as they benefit from lack of it, or if their equity lens interprets minority students as academically at risk. B.4 Desired Outcomes and Goals There seemed to be different educational goals for high and low-income students. B.5 What is STEM and Why We Need to Fill STEM Pipeline NA SkillsS.1 Knowing What Can Be Known Before we started working with the School Improvement Team, they did not know that you could build a data file of academic data that included the scores of all 6th graders, regardless of where they attended elementary school, and merge in the math placement recommendations. We had to type the placement recommendations in because they were all on paper. From this, we could compare standardized test scores to placement recommendations. S.2 How to Identify Kids to Align Services They had previously placed students based on the recommendations of the previous teachers. We surveyed the 5th grade teachers to ask how they decided where to place students. There were as many answers as their were teachers. The principal had previously believed teachers were using a valid data-based way to make the placements. (Some examples of survey answers included a few teachers who reported that they never tracked girls high because they would then be in classes later with older boys, many said that they do not track at-risk students high no matter their scores, and they placing them in standard courses gave them more time to build a stronger foundation. S.3 How to Classify Things NA S.4 Which skill was reflected from the "Skill Set Required for Working With Data" category? Although this principal had better data skills than most that we work with, he did not have the skills for creating the data set that we made for him to compare past academic scores and recommended course placements. S.5 Which skill was reflected from the "Understanding Data Details" category? NA S.6 Understanding Federal DATA-Handling Laws NA
