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Program and policy effects
Prior research has found that financial investments in North Carolina’s early childhood education programs—Smart Start and NC Pre-K—generated positive effects on student achievement in reading and mathematics through eighth grade (Bai et al., 2020). The current study examined if these effects were moderated by two dimensions of educational opportunity in NC public school districts, as measured by (1) the average academic achievement level in third grade and (2) the rate of growth in academic achievement from third to eighth grade. The Smart Start effect on eighth grade reading achievement was larger in school districts with higher levels of average achievement. Also, the NC Pre-K effect on eighth grade reading achievement was smaller in school districts with higher rates of achievement growth.
In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This process is both time and labor-intensive, which creates a persistent barrier for large-scale assessments of text. Furthermore, enriching ones understanding of a found impact on text outcomes via secondary analyses can be difficult without additional scoring efforts. Machine-based text analytic and data mining tools offer one potential avenue to help facilitate research in this domain. For instance, we could augment a traditional impact analysis that examines a single human-coded outcome with a suite of automatically generated secondary outcomes. By analyzing impacts across a wide array of text-based features, we can then explore what an overall change signifies, in terms of how the text has evolved due to treatment. In this paper, we propose several different methods for supplementary analysis in this spirit. We then present a case study of using these methods to enrich an evaluation of a classroom intervention on young children’s writing. We argue that our rich array of findings move us from “it worked” to “it worked because” by revealing how observed improvements in writing were likely due, in part, to the students having learned to marshal evidence and speak with more authority. Relying exclusively on human scoring, by contrast, is a lost opportunity.
Paraeducators perform multiple roles in U.S. classrooms, including among others preparing classroom activities, working with students individually and in small groups, supporting individualized programming for students with disabilities, managing classroom behavior, and engaging with parents and communities. Yet, little research provides insights into this key group of educators. This study combines an analysis of national administrative data to describe the paraeducator labor market with a systematic review of collective bargaining agreements and other job-defining documents in ten case-study districts. We find a large and expanding labor market of paraeducators, far more diverse along ethnic and racial lines than certified teachers but with far lower wages, fewer performance incentives, less professional development, and fewer opportunities for advancement within the profession.
Nearly half of students who enter college do not graduate. The majority of efforts to increase college completion have focused on supporting students before or soon after they enter college, yet many students drop out after making significant progress towards their degree. In this paper, we report results from a multi-year, large-scale experimental intervention conducted across five states and 20 broad-access, public colleges and universities to support students who are late in their college career but still at risk of not graduating. The intervention provided these “near-completer” students with personalized text messages that encouraged them to connect with campus-based academic and financial resources, reminded them of upcoming and important deadlines, and invited them to engage (via text) with campus-based advisors. We find little evidence that the message campaign affected academic performance or attainment in either the full sample or within individual higher education systems or student subgroups. The findings suggest low-cost nudge interventions may be insufficient for addressing barriers to completion among students who have made considerable academic progress.
Personnel evaluation systems have historically failed to identify and remediate low-performing teachers. In 2012, Chicago Public Schools implemented an evaluation system that incorporated remediation and dismissal plans for low-rated teachers. Regression discontinuity estimates indicate that the evaluation reform increased the exit of low-rated tenured teachers by 50 percent. The teacher labor supply available to replace low-rated teachers was higher performing on multiple dimensions, and instrumental variables estimates indicate that policy-induced exit of low-rated teachers significantly improved teacher quality in subsequent years. Policy simulations show that the teacher labor supply in Chicago is sufficient to remove significantly more low-performing teachers.
We assess whether a light-touch intervention can increase socioeconomic and racial diversity in undergraduate Economics. We randomly assigned over 2,200 students a message with basic information about the Economics major; the basic message combined with an emphasis on the rewarding careers or financial returns associated with the major; or no message. Messages increased the proportion of first generation or underrepresented minority (URM) students majoring in Economics by five percentage points. This effect size was sufficient to reverse the gap in Economics majors between first generation/URM students and students not in these groups. Effect sizes were larger and more precise for better-performing students and first generation students. Extrapolating to the full sample, the treatment would double the proportion of first generation and underrepresented minority students majoring in Economics.
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 24% and present suggestive evidence that our tool also improves students’ satisfaction with the course. These results demonstrate the promise of our tool to complement existing efforts in teachers’ professional development.
Educators must balance the needs of students who start the school year behind grade level with their obligation to teach grade-appropriate content to all students. Educational software could help educators strike this balance by targeting content to students’ differing levels of mastery. Using a regression discontinuity design and detailed software log and administrative data, we compare two versions of an online mathematics program used by students in three education agencies. We find that although students assigned the modified curriculum did progress through content objectives more quickly than students assigned the default curriculum, they did not perform better on pre- and post-objective quizzes embedded in the software, and most never progressed far enough to reach the grade-level content. Furthermore, there was no statistically significant effect of the modified curriculum on formative test scores. These findings suggest policymakers and practitioners should exercise caution when assigning exclusively remedial content to students who start the school year behind grade level, even though this is a common feature of many math educational software programs.