Discussions throughout education, government, and media often discuss the achievement gap between white, black, and Lationo/a students. Many times these discussions focus around what is wrong with education, but do little to offer suggestions for closing the achievement gaps between these groups of students (Rousseau & Tate, 2007). In order to engineer change in mathematics, the first objective for a district, school system, school, or teacher is to believe that all students are capable of learning, doing, and understanding advanced mathematics. This shift in thought can be found within the mathematics education community and nation through documents from the National Council for Teachers of Mathematics standards documents and the passing of the No Child Left Behind Act of 2001 (Rousseau & Tate, 2007).
Trends in mathematical achievement for minority and white students have increased every year from 1973 to 1999 which is very promising; however, significant differences between student achievements still exist. Along with these trends of achievement come trends of how the United States population is changing at the same time (Rousseau & Tate, 2007). Where white students were once a large majority, 78% in 1972, of the population this number has shifted to a majority by 58% (Rousseau & Tate, 2007). This shift comes largely in light of an increasing immigrant population and a decreasing white population (Rousseau & Tate, 2007). Though this analysis of the current state of the nation is where much effort and discussion stops, it is important for the state of the nation, these increasing populations, and mathematics as a whole to engineer change in students learning of mathematics.
In order for districts to adequately address factors that may contribute to student achievement such as race, ethnicity, socio-economic status, etc. there needs to be a shift in student progress monitoring (Rousseau & Tate, 2007). Many districts rely solely on standardized testing results that are many times disseminated after policies and decisions have been carried out by school systems (Rousseau & Tate, 2007). District leaders should also be able to aggregate data in order to implement and monitor changes within their districts which is extremely overlooked throughout the nation (Rousseau & Tate, 2007). Aggregation of student data is important to understand if differences exist and if program implementations foster closing these differences.
Policy decisions made at district levels also do much to control the opportunity for students to learn mathematics, such as quality, time, and design (Rousseau & Tate, 2007). Tracking policies within school systems are many times justified as a means to promote growth of students who are behind and accelerate those that are ahead; however, research has showed and is showing that these practices do little to foster either (Rousseau & Tate, 2007). Decisions such as this jeopardize the quality of mathematics instruction that is being given to different students (Rousseau & Tate, 2007).
Districts serving large minority populations often have large teacher turnover rates (Rousseau & Tate, 2007). Teachers serving in these schools are many times emergency hires who lack teacher certification or new teachers with very little experience (Rousseau & Tate, 2007). Teachers who teach in these areas for extended times often seek transfers to more affluent areas in their districts starting the process over for schools serving minority students in essence jeopardizing the quality of instruction within these schools (Rousseau & Tate, 2007). With these issues in mind, schools serving large percentages of minority students are encouraged to create curriculum materials that are easy to implement, understand, and rigorous while at the same time providing resources and professional development that seamlessly integrate with the curriculum (Rousseau & Tate, 2007). Providing this for new teachers and under qualified teachers can promote better teaching with less planning time (Rousseau & Tate, 2007). Constructs such as these highlight opportunity to learn (OTL) variables such as content exposure and emphasis variables (Rousseau & Tate, 2007). Districts and states should look more deeply into ways to retain teachers in minority school districts to promote OTL variables such as quality of instructional delivery (Rousseau & Tate, 2007).
The quality of teachers in a school provides correlates strongly with student achievement (Rousseau & Tate, 2007). High performing schools spend more time in mathematical discourse, provide ongoing professional development, design rigorous curriculums with high expectations of all students, and address underachievement of low performing students (Rousseau & Tate, 2007). Ongoing, quality professional development is key to helping teachers undergo fundamental changes in their pedagogy (Rousseau & Tate, 2007). The creation of curriculum materials and opportunities for collaboration between teachers, researchers, and master teachers that focuses on student understanding like the project IMPACT, MARS, and QUASAR can have significant increases in student achievement and teacher quality (Rousseau & Tate, 2007).
Though increases have been seen in relationship to student achievement across racial groups, there is still much room for improvement (Rousseau & Tate, 2007). Shifting focus from what is the problem to what can be done to fix the problem can be paramount for those students traditionally underserved. I believe the achievement gaps prevalent in today’s literature such as controlled for by race, ethnicity, SES, parental achievement, etc. are just easy categories that researchers use to aggregate students test scores. I believe if an in-depth qualitative analysis of students’ culture could control for practically all of these typically aggregated fields. I believe this link would center around what is valued and expected in particular households and classrooms.
Tate, W. & Rousseau, C. (2007). Engineering change in mathematics education. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 1209-1246). Charlotte, NC: Information Age.