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Despite documented benefits to college completion, more than a third of students who initially enroll in college do not ultimately earn a credential. Completing college requires students to navigate both institutional administrative tasks (e.g., registering for classes) and academic tasks within courses (e.g., completing homework). In postsecondary education, several promising interventions have shown that text-based outreach and communication can be a low-cost, easy to implement, and effective strategy for supporting administrative task navigation. In this paper, we report on a randomized controlled trial testing the effect of a text-based chatbot with artificial intelligence (AI) capability on students' academic task navigation. We find the academic chatbot significantly shifted students’ final grades, increasing the likelihood students received a course grade of B or higher by eight percentage points. We find large and significant treatment effects for first-generation students, estimating the intervention increased their final course grades by about 11 points on a 100-point scale (and a 16 percentage point increase in earning a B or higher) as well as their completion of and performance on individual course deliverables (e.g., readings, activities, exams).
While a growing body of literature has documented the negative impacts of exclusionary punishments, such as suspensions, on academic outcomes, less is known about how teachers vary in disciplinary behaviors and the attendant impacts on students. We use administrative data from North Carolina elementary schools to examine the extent to which teachers vary in their use of referrals and investigate the impact of more punitive teachers on student attendance and achievement. We also estimate the effect of teachers' racial bias in the use of referrals on student outcomes. We find more punitive teachers increase student absenteeism and reduce student achievement. Moreover, more punitive teachers negatively affect the achievement of students who do not receive disciplinary sanctions from the teacher. Similarly, while teachers with a racial bias in the use of referrals do not negatively affect academic outcomes for White students, they significantly increase absenteeism and reduce achievement for Black students. The results suggest punitive disciplinary measures do not aid teachers in productively managing classrooms; rather, teachers taking more punitive stances may undermine student engagement and learning. Moreover, bias in teachers' referral usage contributes to inequities in student outcomes.
Early research on the returns to higher education treated the postsecondary system as a monolith. In reality, postsecondary education in the United States and around the world is highly differentiated, with a variety of options that differ by credential (associates degree, bachelor’s degree, diploma, certificate, graduate degree), the control of the institution (public, private not-for-profit, private for-profit), the quality/resources of the institution, field of study, and exposure to remedial education. In this Chapter, we review the literature on the returns to these different types of higher education investments, which has received increasing attention in recent decades. We first provide an overview of the structure of higher education in the U.S. and around the world, followed by a model that helps clarify and articulate the assumptions employed by different estimators used in the literature. We then discuss the research on the return to institution type, focusing on the return to two-year, four-year, and for-profit institutions as well as the return to college quality within and across these institution types. We also present the research on the return to different educational programs, including vocational credentials, remedial education, field of study, and graduate school. The wide variation in the returns to different postsecondary investments that we document leads to the question of how students from different backgrounds sort into these different institutions and programs. We discuss the emerging research showing that lower-SES students, especially in the U.S., are more likely to sort into colleges and programs with lower returns as well as results from recent U.S.-based interventions and policies designed to support success among students from disadvantaged backgrounds. The Chapter concludes with some broad directions for future research.
Although enrollment at California’s four-year public universities mostly remained unchanged by the pandemic, the effects were substantial for students at California Community Colleges, the largest higher education system in the country. This paper provides a detailed analysis of how the pandemic impacted the enrollment patterns, fields of study, and academic outcomes of these students through the first four semesters after it started. Consistent with national trends, enrollment dropped precipitously during the pandemic – the total number of enrolled students fell by 11 percent from fall 2019 to fall 2020 and by another 7 percent from fall 2020 to fall 2021. The California Community College system lost nearly 300,000 students over this period. Our analysis reveals that enrollment reductions were largest among African-American and Latinx students, and were larger among continuing students than first-time students. We find no evidence that having a large online presence prior to the pandemic protected colleges from these negative effects. Enrollment changes were substantial across a wide range of fields and were large for both vocational courses and academic courses that can be transferred to four-year institutions. In terms of course performance, changes in completion rates, withdrawal rates, and grades primarily occurred in the spring of 2020. These findings of the effects of the pandemic at community colleges have implications for policy, impending budgetary pressures, and future research.
Black and Latinx students are under-represented in Advanced Placement (AP) and Dual Enrollment (DE), and implicit bias of educators has been discussed as one potential contributing factor. In this study, I test whether implicit and explicit racial bias are related to AP and DE participation and racial/ethnic gaps in participation, controlling for various observable contextual factors. I find a small relationship between implicit racial bias and disparate AP participation for Black students relative to White students, and suggestive evidence of a relationship between explicit racial bias and disparate DE participation for Black students relative to White students. Further, more explicitly-biased communities tend to have lower AP participation rates overall. Implications for school leaders regarding implicit bias training and other ways to address systemic inequities in access are discussed.
We synthesize and critique federal fiscal policy during the Great Recession and Covid-19 pandemic. First, the amount of aid during both crises was inadequate to meet policy goals. Second, the mechanisms used to distribute funds was disconnected from policy goals and provided different levels of aid to districts with equivalent levels of economic disadvantage. Third, data tools are missing making it difficult to understand whether funds were used to meet policy goals. Details for these results are provided along with policy recommendations.
We show that natural disasters affect a region’s aggregate human capital through at least four channels. In addition to causing out-migration, natural disasters reduce student achievement, lower high school graduation rates, and decrease post-secondary attendance. We estimate that disasters that cause at least $500 in per capita property damage reduce the net present value (NPV) of an affected county’s human capital by an average of $505 per person. These negative effects on human capital are not restricted to large disasters: less severe events – disasters with property damages of $100-$500 per capita – also cause significant and persistent reductions in student achievement and post-secondary attendance.
‘QuantCrit’ (Quantitative Critical Race Theory) is a rapidly developing approach that seeks to challenge and improve the use of statistical data in social research by applying the insights of Critical Race Theory. As originally formulated, QuantCrit rests on five principles; 1) the centrality of racism; 2) numbers are not neutral; 3) categories are not natural; 4) voice and insight (data cannot ‘speak for itself); and 5) a social justice/equity orientation (Gillborn et al, 2018). The approach has quickly developed an international and interdisciplinary character, including applications in medicine (Gerido, 2020) and literature (Hammond, 2019). Simultaneously, there has been ferocious criticism from detractors outraged by the suggestion that numbers are anything other than objective and scientific (Airaksinen, 2018). In this context it is vital that the approach establishes some common understandings about good practice; in order to sustain rigor, make QuantCrit accessible to academics, practioners, and policymakers alike, and resist widespread attempts to over-simplify and pillory. This paper is intended to advance an iterative process of expanding and clarifying how to ‘QuantCrit’.
This article reviews the development of my thesis that the California Supreme Court's Serrano decisions, which began in 1971 and sought to disconnect district school spending with local property taxes, led to the fiscal conditions that caused California voters to embrace Proposition 13 in 1978, which radically undermined the local property tax system. I submit that my thesis is most likely true because of Proposition 13’s durability and the absence of alternative explanations that account for its longstanding power over California politics. The article then circles back to John Serrano himself. I want to respectfully suggest that John’s views about the role of public education and my own have more in common than might be suspected. At the very least I want to correct the impression that John supported Proposition 13, which was suggested by the title of my last full article about this topic.
In very low-income settings, how much does family demand matter for child learning? In rural Gambia, caregivers with high aspirations for their children’s future education and career, measured before children start school, invest substantially more than other families in their children’s education. Despite this, essentially no children are literate or numerate three years later. In contrast, in villages receiving a highly impactful, teacher-focused supply-side intervention, children of high-aspirations caregivers are 25 percent more likely to achieve literacy and numeracy than other children. In such settings, greater demand can map onto developmentally meaningful learning differences, but only with adequate complementary inputs.