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 evaluating IDEA (Individuals Dedicated to Excellence and Achievement) Public Schools’ implementation of two computer science interventions that aim to (a) increase access to and participation in rigorous mathematics and computer science coursework among students who are traditionally underrepresented and (b) increase the number of teachers with deep content knowledge in computer science and STEM (science, technology, engineering, and mathematics) within schools predominantly consisting of students from low-income backgrounds. The goals of the evaluation are to determine if the two interventions are improving students’ performance on district, state, and Advanced Placement mathematics assessments and if they contribute to postsecondary STEM aspirations.
AIR conducted an evaluation of the Say Yes to Education Syracuse City School District Program, a citywide collaborative intervention aimed at improving educational outcomes and educational attainment for all Syracuse students. The goals of the evaluation were to examine broad K-12 student outcomes in the context of the Say Yes program and to address the multiple barriers to college access in urban populations characterized by socioeconomic disadvantage.
AIR’s NAEP researchers are conducting statistical and psychometric research, evaluation, and data analysis in support of the National Center for Education Statistics (NCES). The goals of the research studies conducted by AIR under this task include (a) developing the psychometric soundness and precision of NAEP assessments, (b) adapting NAEP assessments to changing situations and populations, (c) enhancing understanding of NAEP results, and (d) exploring new ways of modeling NAEP data.