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We leverage log data from an educational app and two-way text message records from over 3,500 students during the summers of 2019 and 2020, along with in-depth interviews in Spanish and English, to identify patterns of family engagement with educational technology. Based on the type and timing of technology use, we identify several distinct profiles of engagement, which we group into two categories: Independent Users who engage with technology-based educational software independently, and Interaction-Supported Users who use two-way communications to support their engagement. We also find that as the demands of families from schools increased during the COVID-19 pandemic, Spanish-speaking families were significantly more likely than English-speaking families to engage with educational technology across all categories of families, particularly as Interaction-Supported Users.
School finance court cases have proceeded one or more times in all but two states. Plaintiffs ask the courts to rule that the existing funding formula is unconstitutional under state constitutions, and the defendants call for continuation of the existing finance formula. By compiling and analyzing the universe of such cases, we can accurately describe the nature of the cases, the decisions made, and the long run impact on overall financing of schools. Defendants win a slight majority of decisions with, surprisingly, their victories coming most frequently in low spending states and in low achieving states. And, while plaintiff victories on average yield an immediate increase in funding, they have no influence on long run growth in school spending.
Low-socioeconomic status (SES), minority, and male students perform worse than their high-SES, non-minority, and female peers on standardized tests. This paper investigates how within-school differences in school quality contribute to these educational achievement gaps. Using individual-level data on the universe of public-school students in California, I estimate school quality using a value added methodology that accounts for the fact that students sort to schools on observable characteristics. I allow for within-school heterogeneity by estimating a distinct value added for each school's low-/high-SES, minority/non-minority, and male/female students. Standard value added models suggest that on average schools provide less value added to their low-SES, minority, and male students, particularly on postsecondary enrollment. However, value added models that control for neighborhood, older-sibling, and peer characteristics suggest that schools provide similar value added to low-/high-SES students and minority/non-minority students but more value added to female students. Within-school heterogeneity accounts for 6% of the test-score achievement gap and 22% of the difference in postsecondary enrollment between men and women.
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.