This journal article discusses findings from a study in which researchers developed a machine learning classifier to predict nontraditional student dropout.
AIR, in collaboration with IMPAQ, is designing and building a borrower-based dynamic microsimulation model of the repayment of federal student loans for the Cost Estimation and Analysis Division of the U.S. Department of Education's Office of Budget Services. This work will help the Department better estimate the costs and consequences of student loan debt for a wide array of student populations, as well as understand the impact of potential policy changes on loan program costs and student outcomes.
AIR is partnering with the Arkansas Department of Education to identify which student-level K–12 indicators are best suited for predicting postsecondary success. The aims of this research are twofold to better understand the malleable conditions and characteristics that place students at risk of not attaining postsecondary readiness and to use data to support timely and targeted interventions designed to enhance students' odds of experiencing later life success.
Researchers at the Regional Educational Laboratory Southwest, operated by AIR, in collaboration with the Texas Hispanic STEM Research Alliance, conducted a study to identify associations between predictive indicators and postsecondary science, technology, engineering, and math (STEM) success among Hispanic students in Texas. The goals of the study were to identify factors that predict positive STEM-related postsecondary outcomes for students in Texas and to determine whether the association between predictive factors and outcomes differs between Hispanic and non-Hispanic White students.