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Program and policy effects
Career and technical education (CTE) has existed in the United States for over a century, and only in recent years have there been opportunities to assess the causal impact of participating in these programs while in high school. To date, no work has assessed whether the relative costs of these programs meet or exceed the benefits as described in recent evaluations. In this paper, we use available cost data to compare average costs per pupil in standalone high school CTE programs in Connecticut and Massachusetts to the most likely counterfactual schools. Under a variety of conservative assumptions about the monetary value of known educational and social benefits, we find that programs in Massachusetts offer clear positive returns on investment, whereas programs in Connecticut offer smaller, though mostly non-negative expected returns. We also consider the potential cost effectiveness of CTE programs offered in other contexts to address questions of generalizability.
Schools often track students to classes based on ability. Proponents of tracking argue it is a low-cost tool to improve learning since instruction is more effective when students are more homogeneous, while opponents argue it exacerbates initial differences in opportunities without strong evidence of efficacy. In fact, little is known about the pervasiveness or determinants of ability tracking in the US. To fill this gap, we use detailed administrative data from Texas to estimate the extent of tracking within schools for grades 4 through 8 over the years 2011-2019. We find substantial tracking; tracking within schools overwhelms any sorting by ability that takes place across schools. The most important determinant of tracking is heterogeneity in student ability, and schools operationalize tracking through the classification of students into categories such as gifted and disabled and curricular differentiation. When we examine how tracking changes in response to educational policies, we see that schools decrease tracking in response to accountability pressures. Finally, when we explore how exposure to tracking correlates with student mobility in the achievement distribution, we find positive effects on high-achieving students with no negative effects on low-achieving students, suggesting that tracking may increase inequality by raising the ceiling.
Using daily lunch transaction data from NYC public schools, I determine which students frequently stand next to one another in the lunch line. I use this `revealed' friendship network to estimate academic peer effects in elementary school classrooms, improving on previous work by defining not only where social connections exist, but the relative strength of these connections. Equally weighting all peers in a reference group assumes that all peers are equally important and may bias estimates by underweighting important peers and overweighting unimportant peers. I find that students who eat together are important influencers of one another's academic performance, with stronger effects in math than in reading. Further exploration of the mechanisms supports my claim that these are friendship networks. I also compare the influence of friends from different periods in the school year and find that connections occurring around standardized testing dates are most influential on test scores.
Reclassification can be an important juncture in the academic experience of English Learners (ELs). Literature has explored the potential for reclassification to influence academic outcomes like achievement, yet its impact on social-emotional learning (SEL) skills, which are as malleable and important to long-term success, remains unclear. Using a regression discontinuity design, we examine the causal effect of reclassification on SEL skills (self-efficacy, growth mindset, self-management, and social awareness) among 4th to 8th graders. In the districts studied, reclassification improved academic self-efficacy by 0.2 standard deviations for students near the threshold. Results are robust to alternative specifications and analyses. Given this evidence, we discuss ways districts might establish practices that instill more positive academic beliefs among ELs.
Responsive teaching is a highly effective strategy that promotes student learning. In math classrooms, teachers might funnel students towards a normative answer or focus students to reflect on their own thinking, deepening their understanding of math concepts. When teachers focus, they treat students’ contributions as resources for collective sensemaking, and thereby significantly improve students’ achievement and confidence in mathematics. We propose the task of computationally detecting funneling and focusing questions in classroom discourse. We do so by creating and releasing an annotated dataset of 2,348 teacher utterances labeled for funneling and focusing questions, or neither. We introduce supervised and unsupervised approaches to differentiating these questions. Our best model, a supervised RoBERTa model fine-tuned on our dataset, has a strong linear correlation of .76 with human expert labels and with positive educational outcomes, including math instruction quality and student achievement, showing the model’s potential for use in automated teacher feedback tools. Our unsupervised measures show significant but weaker correlations with human labels and outcomes, and they highlight interesting linguistic patterns of funneling and focusing questions. The high performance of the supervised measure indicates its promise for supporting teachers in their instruction.
Prior research has found that financial investments in North Carolina’s pre-kindergarten (pre-K) program generated positive effects on student reading and math achievement through eighth grade (Bai et al., 2020). The current study examined the interaction between NC Pre-K funding and two key dimensions of the subsequent educational environment students experience in their school districts: average achievement and achievement growth. In relation to student reading and math achievement in eighth grade, the benefits of NC Pre-K funding were found to be additive to the benefits of school-district average achievement. The benefits of NC Pre-K funding were also found to interact with the benefits of school-district achievement growth such that the NC Pre-K effect was larger in school districts with lower rates of growth in academic achievement. These findings suggest that public investments in early childhood education may be particularly beneficial in the long term for children who subsequently experience low-growth schooling environments compared to children in high-growth environments.
Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource intensive in most educational contexts. We develop an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage teaching practice that supports dialogic instruction and makes students feel heard. We conduct a randomized controlled trial as part of an online computer science course, Code in Place (n=1,136 instructors), to evaluate the effectiveness of the feedback tool. We find that the tool improves instructors’ uptake of student contributions by 27% and present suggestive evidence that our tool also improves students’ satisfaction with the course and assignment completion. These results demonstrate the promise of our tool to complement existing efforts in teachers’ professional development.
Graduate education is among the fastest growing segments of the U.S. higher educational system. This paper provides up-to-date causal evidence on labor market returns to Master’s degrees and examines heterogeneity in the returns by field area, student demographics and initial labor market conditions. We use rich administrative data from Ohio and an individual fixed effects model that compares students’ earnings trajectories before and after earning a Master’s degree. Findings show that obtaining a Master’s degree increased quarterly earnings by about 12% on average, but the returns vary largely across graduate fields. We also find gender and racial disparities in the returns, with higher average returns for women than for men, and for White than for Black graduates. In addition, by comparing returns among students who graduated before and under the Great Recession, we show that economic downturns appear to reduce but not eliminate the positive returns to Master’s degrees.
For decades, pundits, politicians, college administrators, and academics have lamented the dismal rates of civic engagement among students who enroll in courses and eventually major in science, technology, engineering, and mathematics (i.e., STEM) fields. However, the research supporting this conclusion has faced distinct challenges in terms of data quality. Does STEM actually decrease the odds that young people will be actively involved in democracy? This paper assesses the relationship between studying STEM and voting. To do so, we create a dataset of over 23 million students in the U.S. matched to national validated voting records. The novel dataset is the largest known individual-level dataset in the U.S. connecting high school and college students to voting outcomes. It also contains a rich set of demographic and academic variables, to account for many of the common issues related to students' selection into STEM coursework. We consider two measures of STEM participation ---Advanced Placement (AP) Exam taking in high school and college major. Using both measures, we find that, unconditionally, STEM students are slightly more likely to vote than their non-STEM peers. After including the rich set of controls, the sign reverses and STEM students are slightly less likely to vote than their non-STEM peers. However, these estimated relationships between STEM and voting are small in magnitude---about the same effect size as a single get-out-the-vote mailer---and we can rule out even very modest causal effects of marginally more STEM coursework on voting for the typical STEM student. We cannot rule out modest effects for a few subfields. Our analyses demonstrate that, on average, marginally more STEM coursework in high school and college does not contribute to the dismally low participation rates among young people in the U.S.