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Methodology, measurement and data
A controversial, equity-focused mathematics reform in the San Francisco Unified School District (SFUSD) featured delaying Algebra I until ninth grade for all students. This descriptive study examines student-level longitudinal data on mathematics course-taking across successive cohorts of SFUSD students who spanned the reform’s implementation. We observe large changes in ninth and tenth grades (e.g., delaying Algebra I and Geometry). Participation in Advanced Placement (AP) math initially fell 15% (6 pp.) driven by declines in AP Calculus and among Asian/Pacific-Islander students. However, growing participation in acceleration options attenuated these reductions. Large ethnoracial gaps in advanced math course-taking remained.
A systematic review of the literature (1965–2022) and meta-analysis were undertaken to compare the school readiness skills of children participating in public pre-kindergarten (pre-K) or Head Start. Seven quasi-experimental studies met the inclusion criteria for the meta-analysis and 38 effect sizes were analyzed. Results indicated no reliable meta-analytic effect in relation to children’s school readiness skills overall nor in relation to language, mathematics, or social-behavioral skills specifically. A small, positive meta-analytic effect favoring public pre-K compared to Head Start participation was found in relation to children’s emergent literacy skills (Hedges’ g = 0.17). Strategies are discussed to further equate the benefits of public pre-K and Head Start programming by facilitating greater cross-sector collaboration.
We administer a survey to study students' preferences for relative performance feedback in an introductory economics class. To do so, we elicit students' willingness to pay for/avoid learning their rank on a midterm exam. Our results show that 10% of students are willing to pay to avoid learning their rank. We also find that female students are willing to pay $1 more than male students. We also confirm that beliefs about academic performance do not predict preferences for information. After randomizing access to information about rank, students report needing more study hours to achieve their desired grade and being less likely in the top half of the ability distribution in the class. These effects are driven by stronger effects from people who overestimated their midterm rank compared to those who underestimated their performance. We do not find an overall effect of learning about rank performance on final course grade. We also confirm that students' preferences for feedback do not interfere with their belief updating.
At schools with low grading standards, students receive higher school-awarded grades across multiple courses than students with the same skills receive at schools with high grading standards. A new methodology shows grading standards vary substantially, certainly enough to affect post-secondary opportunities, across high schools in Alberta. Schools with low grading standards are more likely to be private, rural, offer courses for students returning to high school, have smaller course cohorts, have a smaller percentage of lone parent households and a larger percentage of well-educated parents. Variation in grading standards changes post-secondary opportunities in systematic ways.
Our study examines roughly 2,000 novice teachers’ responses about how they account for students’ cultural, ethnic/racial, and linguistic diversity. We qualitatively analyze robust open-ended survey responses to explore teachers’ reported strategies for how they integrate asset-based pedagogy (ABP). We identify codes related to these strategies and then investigate them by participant demographics. This illuminates both the predictive validity of our qualitative analyses as well as provides initial evidence as to whether certain characteristics are associated with critical techniques. Our findings inform practitioners of a suite of ABP strategies as well as districts and policymakers about how novice teachers are processing asset-based instruction and who to target support in this vital pedagogical area.
Given the spike of homicides in conflict zones of Colombia after the 2016 peace agreement, I study the causal effect of violence on college test scores. Using a difference-in-difference design with heterogeneous effects, I show how this increase in violence had a negative effect on college learning, and how this negative effect is mediated by factors such as poverty, college major, degree type, and study mode. A 10% increase in the homicide rate per 100,000 people in conflict zones of Colombia, had a negative impact on college test scores equivalent to 0.07 standard deviations in the English section of the test. This negative effect is larger in the case of poor and female students who saw a negative effect of approximately 0.16 standard deviations, equivalent to 3.4 percentage points out of the final score. Online and short-cycle students suffer a larger negative effect of 0.14 and 0.19 standard deviations respectively. This study provides among the first evidence of the negative effect of armed conflict on college learning and offers policy recommendations based on the heterogeneous effects of violence.
Teachers’ sense-making of student behavior determines whether students get in trouble and are formally disciplined. Status categories, such as race, can influence perceptions of student culpability, but the degree to which teachers’ initial identification of student misbehavior exacerbates racial disproportionality in discipline receipt is unknown.This study provides the first systematic documentation of teachers’ use of office discipline Referrals (ODRs) in a large, diverse urban school district in California that specifies the identity of both the referred and referring individuals in all ODRs. We identify teachers exhibiting extensive referring behavior, or the top 5 percent referrers based on the number of ODRs they make in a given year and evaluate their contributions to disciplinary disparities. We find that “top referrers” effectively double the racial gaps in ODRs for both Black-White and Hispanic-White comparisons. These gaps are mainly driven by higher numbers of ODRs issued for Black and Hispanic students due to interpersonal offences and defiance, and also partially convert to racial gaps in suspensions. Both the level and racial compositions of the school sites where “top referrers” serve and their personal traits seem to explain some of their frequent referring behavior. Targeting supports and interventions to “top referrers” might afford an important opportunity to reduce racial disciplinary gaps
Lottery-based identification strategies offer potential for generating the next generation of evidence on U.S. early education programs. Our collaborative network of five research teams applying this design in early education and methods experts has identified six challenges that need to be carefully considered in this next context: 1) available baseline covariates may not be very rich; 2) limited data on the counterfactual; 3) limited and inconsistent outcome data; 4) weakened internal validity due to attrition; 5) constrained external validity due to who competes for oversubscribed programs; and 6) difficulties answering site-level questions with child-level randomization. We offer potential solutions to these six challenges and concrete recommendations for the design of future lottery-based early education studies.
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models -- one predicting course completion, the second predicting degree completion. Our results show that algorithmic bias in both models could result in at-risk Black students receiving fewer success resources than White students at comparatively lower-risk of failure. We also find the magnitude of algorithmic bias to vary within the distribution of predicted success. With the degree completion model, the amount of bias is nearly four times higher when we define at-risk using the bottom decile than when we focus on students in the bottom half of predicted scores. Between the two models, the magnitude and pattern of bias and the efficacy of basic bias mitigation strategies differ meaningfully, emphasizing the contextual nature of algorithmic bias and attempts to mitigate it. Our results moreover suggest that algorithmic bias is due in part to currently-available administrative data being less useful at predicting Black student success compared with White student success, particularly for new students; this suggests that additional data collection efforts have the potential to mitigate bias.