How did it get this way? Disentangling the sources of teacher quality gaps through agent-based modeling.

We develop a novel simulation methodology to study the extent to which three interrelated processes-teacher attrition from the state teaching workforce, teacher mobility between teaching positions, and teacher hiring for open positions-contribute to "teacher quality gaps" (TQGs) between students of color and other students in K-12 public schools. We apply this methodology to data from Washington State to provide estimates that eliminating inequities in teacher mobility and hiring across different schools would close TQGs within 5 years, while just eliminating inequities in teacher hiring would close gaps within 10 years. On the other hand, eliminating inequities in teacher attrition without addressing mobility and hiring does little to close gaps.

The role of teachers and schools in explaining STEM outcome gaps.

We develop a novel methodology to explore the extent to which teacher and school assignments between fourth and eighth grade contribute to inequalities between advantaged and disadvantaged students-as defined by underrepresented minority (URM) status and eligibility for free or reduced-price lunch (FRL)-in their eighth-grade math and science test scores and high school math and science course-taking. We find that differences between advantaged and disadvantaged students in teacher and school assignments predict about 25% of the eighth-grade math test gaps, 10% of the eighth-grade science test gaps, and 35% of the gaps in advanced math course-taking. For science course-taking, school and teacher assignments predict about 20% of the URM gap and 10% of the FRL gap. While this methodology does not permit causal conclusions, the results are robust to analyses of a subsample in which students do not appear to be assigned to teachers based on prior test achievement.

Exploring the Impact of Student Teaching Apprenticeships on Student Achievement and Mentor Teachers.

We exploit within-teacher variation in the years that math and reading teachers in grades 4-8 host an apprentice ("student teacher") in Washington State to estimate the causal effect of these apprenticeships on student achievement, both during the apprenticeship and afterwards. While the average causal effect of hosting a student teacher on student performance in the year of the apprenticeship is indistinguishable from zero in both math and reading, hosting a student teacher is found to have modest positive impacts on student math and reading achievement in a teacher's classroom in following years. These findings suggest that schools and districts can participate in the student teaching process without fear of short-term decreases in student test scores while potentially gaining modest long-term test score increases.

Career and Technical Education, Inclusion, and Postsecondary Outcomes for Students With Learning Disabilities.

We used longitudinal data from Washington State to investigate the relationships among career and technical education (CTE) enrollment, inclusion in general education, and high school and postsecondary outcomes for students with learning disabilities. We replicated earlier findings that students with learning disabilities who were enrolled in a "concentration" of CTE courses had higher rates of employment after graduation than observably similar students with learning disabilities who were enrolled in fewer CTE courses. We also found that students with learning disabilities who spent more time in general education classrooms in high school had higher rates of on-time graduation, college attendance, and employment than observably similar students with learning disabilities who spent less time in general education classrooms in these grades.

Is it the intervention or the students? using linear regression to control for student characteristics in undergraduate STEM education research.

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due to the effect of an instructional intervention or to differences in student characteristics when students cannot be assigned to control and treatment groups at random. Using pre- and posttest scores from an introductory biology course, we illustrate how the methods currently in wide use can lead to erroneous conclusions, and how multiple linear regression offers an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. In general, we recommend that researchers always use student-level regression models that control for possible differences in student ability and preparation to estimate the effect of any nonrandomized instructional intervention on student performance.