Li Feng

This study is a path forward for the large-scale, data-driven quantitative analysis of noisy open-source data resources. The goal is to support qualitative findings of smaller studies with extensive open-source data-driven analytics in a new way. The study presented in this research focuses on learning interventions. It uses nine publicly accessible datasets to understand and mitigate factors contributing to learning loss and the practical learning recovery measures in Texas public school districts after the recent school closures. The data came from the Census Bureau 2010, USAFACTS, Texas Department of State Health Services (DSHS), the National Center for Education Statistics (CCD), the U.S. Bureau of Labor Statistics (LAUS), and three sources from the Texas Education Agency (STAAR, TEA, ADA, ESSER). We demonstrate a novel data-driven approach to discover insights from an extensive collection of heterogeneous public data sources. For the pandemic school closure period, the mode of instruction and prior score emerged as the primary resilience factors in the learning recovery intervention method. Grade level and census community income level are the most influential factors in predicting learning loss for both Math and Reading. We demonstrate that data-driven unbiased data analysis at a larger scale can offer policymakers an actionable understanding of how to identify learning-loss tendencies and prevent them in public schools.

Background: Evidence-based and student-centered instructional methods hold the promise of transforming undergraduate STEM education and simultaneously solving the dual challenge of STEM workforce needs and inequities within STEM. The Learning Assistant (LA) Model was created to reform curriculum, recruit teachers, and inform discipline-based education research. Numerous studies have shown positive impacts on students and Learning Assistants (LAs) in terms of retention and pass rates, learning outcomes, attitudinal shifts, and emotional support. Several studies have explored demographic differences and found that LAs help close the gap in retention rates for women and first-generation students. While short-term effects have been well documented, the long-term effects on students’ college retention and completion rates have not been widely studied. We gathered information about LA-adopting institutions identified on the Learning Assistant Alliance website and from individual higher education institutions’ websites. Combining this information with the College Scorecard Database for four-year U.S. institutions allows us to examine student retention and graduation rates after these institutions have adopted the LA model and look for differences in both the student and university characteristics included in the dataset. We are interested in determining if there is a difference between LA-adopting and non-LA-adopting institutions in terms of their student outcomes. Results: These LA-adopting institutions have larger enrollments and are more likely to award graduate degrees. Students in LA-adopting institutions are more likely to be younger with lower percentages of females, first generation, and Black students. No significant differences in the percentages of Hispanic students or in the average costs per student were observed in our study. Using regression analysis, we find LA-adopting institutions have significantly higher retention and completion rates. Conclusions: Since LA-adopting institutions have different characteristics than non-LA institutions, these differences should be considered in future studies examining the impact of the LA model. After controlling for these differences in our study, we find that institutions with the LA model have 4 to 15 percentage points higher college completion rate and they have better outcomes for Hispanic and Black students. These findings suggest that increasing the adoption of the LA model may contribute to reducing inequity in STEM.

The Developing Hispanic-Serving Institution (DHSI) Program is one of the largest federal grant programs that support Hispanic-Serving Institutions. We employ an eleven-year panel dataset of U.S. higher education institutions to implement a difference-in-differences (DID) design, two-way fixed effect model (TWFE), and propensity score matching (PSM) to compare DHSI grantee institutions with other Title V eligible higher education institutions that did not receive DHSI funding. We find that the receipt of DHSI grant funding helps these institutions significantly improve their retention rates (2~ percentage points), completion rates (1-3 percentage points), and reduce their students’ debt level (e.g., $500~$1000 lower for graduated students). Our back-of-the-envelope cost-benefit calculation shows that the DHSI program benefits exceed cost.

Here, we examine over two decades of empirical literature to explore the ways scholars have been working to reveal the changing set of policy and political conditions in which teachers unions are operating. In this context, we identify the conceptual models educational researchers have used to frame their research and the applications of these frameworks in the literature on teachers unions. Our findings reveal that the research on teachers unions over the past two decades provides deep historical context. Less evident in this research are explicit conceptual groundings to theory and how the theoretical/empirical literature is applied in inquiry.

We investigate the effects of a statewide program designed to increase the supply of teachers in designated “hard‐to‐staff” areas, such as special education, math, and science. Employing a difference‐in‐difference estimator we find that the loan forgiveness component of the program was effective, reducing mean attrition rates for middle and high school math and science teachers by 10.4 percent and 8.9 percent, respectively. We also find that the impact of loan forgiveness varied with the generosity of payments; when fully funded, the program reduced attrition of special education teachers by 12.3 percent, but did not have a statistically significant impact when funding was substantially reduced. A triple‐difference estimate indicates that a one‐time bonus program also had large effects, reducing the likelihood of teachers’ exit by as much as 32 percent in the short run. A back‐of‐the‐envelope cost‐benefit analysis suggests that both the loan forgiveness and the bonus program were cost effective.

There is growing concern among policy makers over the quality of the teacher workforce in general, and the distribution of effective teachers across schools. The impact of teacher attrition on overall teacher quality will depend on the effectiveness of teachers who leave the profession. Likewise, teacher turnover may alleviate or worsen inequities in the distribution of teachers, depending on which teachers change schools or leave teaching and who replaces them. Using matched student–teacher panel data from the state of Florida, we examine teacher mobility across the distribution of effectiveness (as measured by teacher value added). We find that top-quartile and bottom-quartile teachers exit at a higher rate than do average-quality teachers. Additionally, as the share of peer teachers with more experience, advanced degrees, or professional certification increases, the likelihood of moving within-district decreases. We also find some evidence of assortative matching among teachers—more productive reading/language arts teachers are more likely to stay in teaching if they have more productive peer teachers.