Factors That Affect Student Outcomes in U.S. Geography Education

Michael Solem and Phillip W. Vaughan

Annals of the American Association of Geographers, 2023, vol. 113, issue 6, 1383-1401

Abstract: The ideal of U.S. public education as “a great equalizer” remains unrealized across large swaths of the country. Young people in schools are at varying levels of educational advantage and disadvantage owing to wide gaps in learning opportunities and disparate access to high-quality curriculum. Unequal educational achievement has also been linked to inequities affecting some students before they enter school due to socioeconomic circumstances, prejudice, and discriminatory social systems and structures. In this study, we begin to partition the various factors that account for inequality in student outcomes in the context of U.S. geography education. Using large-scale data sets provided by the National Center for Education Statistics within the U.S. Department of Education, we developed a two-level statistical model to analyze the extent to which geography achievement in eighth grade varies systematically with contextual opportunity to learn (OTL) factors and the relative poverty level of neighborhoods around schools. Hierarchical linear modeling was used to account for data clustering, producing an achievement estimate for each predictor and controlling for the effects of all other predictors. Statistically significant OTL predictors included instructional exposure, taking geography prior to eighth grade, teaching experience, and the availability of computers in classrooms. Schools located in neighborhoods with higher income-to-poverty ratios outperformed schools in neighborhoods closer to the federal poverty threshold. Controlling for OTL and school neighborhood effects accounted for some of the geography achievement disadvantage associated with race and other student characteristics. Geographers can further explore these relationships with formal mediational models and educational programs based on equity-oriented frameworks.

Date: 2023
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DOI: 10.1080/24694452.2023.2187341

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