Source: Teachers College Record, Volume 112 Number 4, 2010, p. 1064-1095.
Many studies have looked at students’ mathematics achievement in the middle and high school years and the kinds of factors that are associated with their achievement. Within this domain, however, most research utilized cross-sectional data. Cross-sectional designs have both statistical and conceptual limitations.
Few studies used longitudinal data that typically included two time points, occasionally three. A pure longitudinal design is problematic as well because it does not take into account the multilevel nature of data derived from educational settings.
In an attempt to account for differences in mathematics achievement, researchers have advanced different explanations, varying from affective/psychological factors (e.g., math attitude) to social factors (e.g., influences of parents, teachers, and peers). However, because of the division of psychology and sociology, subdivisions within these fields, and specialized individual research interests, a limitation of the research in this literature is that the variables are often studied in isolation rather than in concert. A promising way to resolve this problem, as Herbert Walberg argued in his psychological theory of educational productivity, is to include the chief known correlates of educational achievement derived from experimental and nonexperimental research and simultaneously analyze panel data collected on many individuals over multiple time periods on variables such as age or developmental level; ability, including prior achievement; social environment for learning; and home environment.
Taken together, these studies provide a foundation for studying individual differences in secondary mathematics growth and end-of-high-school mathematics attainment and exploring various psychological and social factors that might predict such differences from a longitudinal and multilevel perspective.
The present study attempted to investigate how high school seniors get to where they are in terms of end-of-high-school mathematics attainment. In addition, the study explored what factors might predict students’ attainment and their growth trajectories in mathematics during secondary school years.
The present study is a secondary analysis of longitudinal data that tracked a national probability sample of seventh graders until they graduated from high school. Three-level hierarchical linear models were fitted to the data using a Level 1 piecewise linear growth model nested within students (Level 2) across schools (Level 3).
One of the important findings of this study was that on average, there was a drop in mathematics achievement during the senior year of high school for students in the sample regardless of student mathematics achievement in Grade 7.
Additionally, the study found an inequitable distribution of mathematics attainment at the end of high school associated with initial differences in mathematics achievement. Several individual, school composition, and opportunities to learn variables, such as early tracking and course progress, were found to be strong predictors of students’ mathematics attainment and growth.
These empirical findings point to the further directions we may take to promote student achievement in mathematics.