Purpose: In this study, the researchers aimed to provide a current and comprehensive description of the relationship between curricular intensity (the number of academic courses students complete and the level or difficulty of those courses) and postsecondary outcomes. The team used recent data from a nationally representative study and a rigorous methodology that adjusts for self-selection issues. They focused on students with disadvantaged backgrounds, such as racial/ethnic minorities (Black, Hispanic), English language learners, students with disabilities, students from different socioeconomic status levels, or first-generation college students. Broadly, the results of the study provided valuable information about the consequences of recent college and career readiness initiatives, such as Algebra for All, Race to the Top, and the Common Core.

Project Activities: The researchers obtained restricted access to data from the High School Longitudinal Study of 2009 (HSLS:09) from the National Center for Education Statistics. The team created measures of curricular intensity and conducted descriptive analyses to reveal the rates of students with access to more intensive curricula and advanced courses, characteristics of these students, whether there are gaps in access, and, if so, where the gaps might exist—for instance, in what type of schools and why, in differences in course offerings, or in the allocation of students to courses. The team also completed a series of inferential analyses to help uncover which patterns of course taking are associated with higher postsecondary success, such as on-time graduation from high school, college enrollment, and the need for remedial course taking.

Key Findings: Findings show that (1) Timing of the course-taking matters; (2) Advanced coursework is important; (3) Students who take diverse courses are likely to have better postsecondary outcomes; and (4) Both the quality and quantity of the coursework matters.

Structured Abstract

Setting: Data came from a nationally representative sample of U.S. high school students who started high school located in rural or urban settings in 2009, followed up in 2012 and 2016.

Sample: The sample for this study came from HSLS:09, using the 2016 Second Follow-Up and prior collections. The HSLS:09 comprises approximately 25,000 students who were in the ninth grade in 2009, selected using a stratified, two-stage random sample design, with schools selected in the first stage and students in the second. The nationally representative sample was contacted again in 2012, when most students were in the 11th grade, and transcript data were collected from their schools in 2013, after most had completed high school. The students were contacted a third time in 2016, 2 years after high school graduation, to learn about their educational and occupational experiences postgraduation. About 17,000 students responded to the 2016 follow-up. The analytical sample included about 11000 students of various demographics and backgrounds, reflecting the nation. The weighted analytical sample was 50 percent female, 2 percent English language learner students, and 32 percent students with a disability. The sample comprised 53 percent White, 13 percent Black, and 4 percent Asian. Roughly 22 percent of the students were Hispanic.

Malleable Factor: The malleable factor in this study was high school curricular intensity.

Research Design and Methods: In this exploratory study using secondary data, the researchers used cluster analysis, latent class analysis, data mining, and data driven approaches to create and compare measures of curricular intensity (see Data Analytic Strategy below). They then conducted (1) descriptive analyses of curricular intensity for the entire sample and subgroups of disadvantaged students and (2) inferential analyses using propensity score stratification to compare outcomes for students with different levels of curricular intensity minimizing selection bias.

Control Condition: There was no control condition. However, the researchers compared the outcomes of students with different levels of curricular intensity.

Key Measures: From variables in the HSLS:09 dataset, the researchers developed measures of curricular intensity to be used in exploratory and inferential analyses. Analyses examined three major outcomes in the dataset: college enrollment, selecting a STEM (science, technology, engineering, mathematics) major, and college without remedial course taking.

Data Analytic Strategy: Using secondary data, the researchers used cluster analysis, latent class analysis, and data mining to create and compare measures of curricular intensity. They  then examined the variation in curricular intensity for student- and school-related factors using multilevel models. Finally, they created propensity scores in which different levels of curricular intensity were modeled as categorical 'treatments" influencing the probability of each of the three outcomes. They also conducted these analyses separately for students from disadvantaged backgrounds.

Products and Publications

ERIC Citations: Find available citations in ERIC for this award here.

Project Website: https://www.air.org/project/why-does-high-school-coursework-matter-case-increasing-exposure-advanced-courses