Search EdWorkingPapers
Search EdWorkingPapers by author, title, or keywords.
Search
K-12 Education
Non-teaching staff comprise over half of all school employees and their turnover may be consequential for school operation, culture, and student success, yet we lack evidence documenting their attrition. We use 11 years of administrative data from Oregon to examine mobility and exit among teachers, administrators, paraprofessionals, and other staff. Although teachers dominate staff turnover conversations, they are consistently the most stable employee group. Some school factors, like the proportion of students being disciplined, predict higher turnover rates for all employees, but within-school turnover between staff groups is weakly correlated and some school context variables are differentially associated with the turnover of various employee groups. Results suggest that employee turnover in schools is not a homogenous phenomenon across staffing groups.
Chetty et al. (2022) say county density of cross-class friendships (referred to here as “adult-bridging capital”) has causal impacts on social mobility within the United States. We instead find that social mobility rates are a function of county density of family capital (higher marriage rates and two-person households), community capital (community organizations, religious congregations, and volunteering), and mean student achievement in grades 3-8. Our models use similar multiple regression equations and the same variables employed by Chetty et al. but also include state fixed effects, student achievement, and family, community, school-bridging (cross-class high school friendships), and political (participation and institutional trust) capital. School-bridging capital is weakly correlated with mobility if adult-bridging is excluded from the model. R-squared barely changes when adult-bridging is incorporated into the model. When it is included, mobility continues to be significantly correlated with the achievement, family, and community variables but not with school-bridging and political ones. We infer that county mobility rates are largely shaped by parental presence, community life, and student achievement. To enhance mobility, public policy needs to enhance the lives of disadvantaged people at home, in school, and in communities, not just the social class of their friendships.
Virtual charter schools are increasingly popular, yet there is no research on the long-term outcomes of virtual charter students. We link statewide education records from Oregon with earnings information from IRS records housed at the US Census Bureau to provide evidence on how virtual charter students fare as young adults. Virtual charter students have substantially worse high school graduation rates, college enrollment rates, bachelor's degree attainment, employment rates, and earnings than students in traditional public schools. Although there is growing demand for virtual charter schools, our results suggest that students who enroll in virtual charters may face negative long-term consequences.
Efforts to attract and retain effective educators in high poverty public schools have had limited success. Dallas ISD addressed this challenge by using information produced by its evaluation and compensation reforms as the basis for effectiveness-adjusted payments that provided large compensating differentials to attract and retain effective teachers in its lowest achievement schools. The Accelerating Campus Excellence (ACE) program offers salary supplements to educators with records of high performance who are willing to work in the most educationally disadvantaged schools. We document that ACE resulted in immediate and sustained increases in student achievement, providing strong evidence that the multi-measure evaluation system identifies effective educators who foster the development of cognitive skills. The improvements at ACE schools were dramatic, bringing average achievement in the previously lowest performing schools close to the district average. When ACE stipends are largely eliminated, a substantial fraction of highly effective teachers leaves, and test scores fall. This highlights the central importance of the performance-based incentives to attract and retain effective educators in previously low-achievement schools.
Although learners are being connected 1:1 with instructors at an increasing scale, most of these instructors do not receive effective, consistent feedback to help them improved. We deployed M-Powering Teachers, an automated tool based on natural language processing to give instructors feedback on dialogic instructional practices —including their uptake of student contributions, talk time and questioning practices — in a 1:1 online learning context. We conducted a randomized controlled trial on Polygence, a re-search mentorship platform for high schoolers (n=414 mentors) to evaluate the effectiveness of the feedback tool. We find that the intervention improved mentors’ uptake of student contributions by 10%, reduced their talk time by 5% and improves student’s experi-ence with the program as well as their relative optimism about their academic future. These results corroborate existing evidence that scalable and low-cost automated feedback can improve instruction and learning in online educational contexts.
We investigated the effectiveness of a sustained and spiraled content literacy intervention that emphasizes building domain and topic knowledge schemas and vocabulary for elementary-grade students. The Model of Reading Engagement (MORE) intervention underscores thematic lessons that provide an intellectual framework for helping students connect new learning to a general schema in Grade 1 (animal survival), Grade 2 (how scientists study past events), and Grade 3 (our human body, a living system that helps us survive). A total of 30 elementary schools (N = 2,870 students) were randomized to a treatment or control condition. In the treatment condition (i.e., full spiral curriculum schools), students participated in content literacy lessons from Grades 1 to 3 during the school year and wide reading of thematically related informational texts in the summer following Grades 1 and 2. In the control condition (i.e., partial spiral curriculum schools), students participated in Grade 3 MORE lessons. Grade 3 lessons for both conditions were implemented online during the COVID-19 pandemic school year. Results reveal that treatment group students outperformed control students on science vocabulary knowledge across all three grades. Furthermore, we found positive transfer effects on Grade 3 science reading (ES = .14), domain-general reading comprehension (ES = .11), and mathematics achievement (ES = .12). Treatment impacts were sustained at 14-month follow-up on Grade 4 reading comprehension (ES = .12) and mathematics achievement (ES = .16). Findings indicate that a content literacy intervention that spirals topics and vocabulary across grades can improve students’ long-term academic achievement outcomes.
A fundamental question for education policy is whether outcomes-based accountability including comprehensive educator evaluations and a closer relationship between effectiveness and compensation improves the quality of instruction and raises achievement. We use synthetic control methods to study the comprehensive teacher and principal evaluation and compensation systems introduced in the Dallas Independent School District (Dallas ISD) in 2013 for principals and 2015 for teachers. Under this far-reaching reform, educator evaluations that are used to support teacher growth and determine salary depend on a combination of supervisor evaluations, student achievement, and student or family survey responses. The reform replaced salary scales based on experience and educational attainment with those based on evaluation scores, a radical departure from decades of rigid salary schedules. The synthetic control estimates reveal positive and significant effects of the reforms on math and reading achievement that increase over time. From 2015 through 2019, the average achievement for the synthetic control district fluctuates narrowly between -0.27 s.d. and -0.3 s.d., while the Dallas ISD average increases steadily from -0.28 s.d. in 2015 to -0.08 s.d. in 2019, the final year of the sample. Though the increase for reading is roughly half as large, it is also highly significant.
When analyzing treatment effects on test score data, education researchers face many choices for scoring tests and modeling results. This study examines the impact of those choices through Monte Carlo simulation and an empirical application. Results show that estimates from multiple analytic methods applied to the same data will vary because, as predicted by Classical Test Theory, two-step models using sum or IRT-based scores provide downwardly biased standardized treatment effect coefficients compared to latent variable models. This bias dominates any other differences between models or features of the data generating process, such as the variability of item discrimination parameters. An errors-in-variables (EIV) correction successfully removes the bias from two-step models. Model performance is not substantially different in terms of precision, standard error calibration, false positive rates, or statistical power. An empirical application to data from a randomized controlled trial of a second-grade literacy intervention demonstrates the sensitivity of the results to model selection and tradeoffs between model selection and interpretation. This study shows that the psychometric principles most consequential in causal inference are related to attenuation bias rather than optimal scoring weights.
U.S. public school students increasingly attend schools with sworn law enforcement officers present. Yet, little is known about how these school resource officers (SROs) affect school environments or student outcomes. Our study uses a fuzzy regression discontinuity (RD) design with national school-level data from 2014 to 2018 to estimate the impacts of SRO placement. We construct this discontinuity based on the application scores for federal school based policing grants of linked police agencies. We find that SROs effectively reduce some forms of violence in schools, but do not prevent gun-related incidents. We also find that SROs intensify the use of suspension, expulsion, police referral, and arrest of students. These increases in disciplinary and police actions are consistently largest for Black students, male students, and students with disabilities.