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patterns. These disparities, or “gaps,” exist along multiple demographic variables (e.g.,
race/ethnicity, SES, English proficiency). While the bulk of extant research focuses on
racial/ethnic disparities, we also address gaps linked to SES and English proficiency.
One widely-cited measure of mathematics learning is the National Assessment of
Educational Progress (NAEP), also known as the “nation’s report card.” For years, NAEP scores
have reflected significant “gaps” in achievement based on race (e.g., J. Lee, 2006) and class
(e.g., Flores, 2007), regardless of grade level. Most recently, on the 2009 NAEP, 75% of White
high school seniors scored at the “Basic” level or better, compared to 36% of Black seniors and
45% of Latina/o seniors (National Center for Education Statistics [NCES], 2011). The gaps are
even wider when only higher-level NAEP items are examined (i.e., those items that require
multi-step problem solving and constructed responses, as opposed to multiple choice); on these
items, 1 in 10 White students was proficient compared with 1 in 30 Latina/o students and 1 in
100 Black students (Haycock, 2001). By the time they finish high school, the average
performance of Black and Latina/o students is not significantly different from that of White 8
th
graders (Haycock, 2001; Lubienski, 2002). Equally striking, on the 2009 NAEP, only 44% of
poor students (those who qualified for the National School Lunch Program) scored at the “Basic”
level or better, compared to 71% of students from families with higher incomes (NCES, 2011).
On the same test, 20% of English learners met or exceeded the “Basic” standard, versus 66% of
non-English learners (NCES, 2011).
Other assessments show similar race- and SES-based patterns (e.g., the Trends in
International Mathematics and Science Study, Gonzales et al., 2004; and the National
Educational Longitudinal Survey, Tate, 1997). Additionally, while child poverty has substantial
effects on mathematics achievement independent of race (Payne & Biddle, 1999), race has
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effects independent of class such that on the 2009 NAEP, White students who were eligible for
the National School Lunch Program still scored better, on average, than Black students who were
not eligible (58% “Basic” or better vs. 45%, respectively) (NCES, 2011; see also Lubienski,
2002).
Similar trends exist for K-12 course-taking patterns. By the 8
th
grade, 49% of Latina/o
students and 47% of Black students have taken algebra or pre-algebra, compared with 68% of
White students (Flores, 2007). Furthermore, beginning in elementary school and all the way
through high school, White students are overrepresented in “gifted,” “honors,” and “advanced
placement” programs, while Black and Latina/o students are severely underrepresented (Darling-
Hammond, 2010; Oakes, 1990; Tyson, 2006). English learners are also frequently blocked from
these tracks (Darling-Hammond, 2010; Olsen, 1997).
Data on students’ identification with and interest in pursuing mathematics are less
available than their standardized test scores, but these measures have significant implications for
who goes on to study the mathematics that is required for college admissions and later, for
STEM careers. Small-scale studies (Boaler & Greeno, 2000) and indirect measures (Catsambis,
1994) suggest that despite achievement gains, girls still have significantly less confidence and
attitudes that are less positive about mathematics than boys. Very little data is available for
similar comparisons by race, class, or English proficiency.
Despite the persistence of race- and SES-based achievement gaps, there have been
marked changes in mathematics achievement since the 1970s. Raw scores have improved for all
racial/ethnic groups on a number of measures, including the NAEP (Lubienski, 2002; Tate,
1997). And yet, although racial/ethnic disparities narrowed through the 1970s and 1980s, that
trend has reversed over the last two decades; gaps have widened since 1988 (Flores, 2007).
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Darling-Hammond (2010) attributes this reversal to the abandonment of policies aimed at
equalizing school funding, raising teacher quality, and eliminating child poverty. In comparison,
recent research has found that current policies emphasizing standardization and high-stakes
accountability (e.g., No Child Left Behind) exacerbate inequity by driving the schools that are
least successful to focus on basic skills as a means of test preparation and to push out students
who are struggling and in need of support (Haney, 2000; McNeil, 2000; Mintrop, 2003; Pedulla,
et al., 2003). This phenomenon disproportionately affects low-SES students and students of color
(Haney, 2000; Rustique-Forrester, 2005).
It was in the context of these national patterns of inequity in mathematics education that
the Railside teachers came together to look for solutions. To some degree, these national patterns
also foreshadow an issue that became an obstacle for Railside teachers in recent years: a national
educational policy climate that focuses heavily on standardization, accountability, and scores on
high-stakes state exams. Next we examine the structural and systemic forces that serve to
maintain the lower levels of achievement in mathematics for students from marginalized groups,
low SES students, and students with limited English proficiency.
PART 2: Inequity in Opportunities to Learn Mathematics
The disparities summarized in the preceding section can be interpreted in at least two
ways. One possible interpretation is that racial/ethnic minorities, low-SES students, and English
learners are somehow either biologically or socially less inclined toward mathematics. Certainly,
such biased ideas have historically been supported by social science research (Hernnstein &
Murray, 1994; Jensen, 1998; Richards, 1997). However, a wealth of empirical evidence refutes
these claims (Gamoran, 2001; Nisbet, 1998).
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