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The Cobb Teaching & Learning System (CTLS) is a digital learning initiative developed for and by the Cobb County School District (CCSD) in Georgia. CTLS became a crucial initiative used by the district to maintain student academic progress during the COVID-19 pandemic. Adopting a mixed-methods approach, this case study seeks to analyze CTLS’s design and implementation, focusing on digital transformation and professional collaboration within CCSD. This case study highlights how CCSD maintains complete ownership in a customized digital learning initiative supported by technology providers.
CTLS’s success comes from its strategic partnership with external technology providers, most notably EdIncites, commitment to professional collaboration, investment in novel technologies, and focus on real-time data. Looking at district-by-district comparisons, Cobb’s level of achievement and learning recovery resembles that of higher performing suburban districts in Georgia as opposed to its closest geographically and demographically comparable peers. Furthermore, 2019-2022 testing data indicates that all GA Milestone End-Of-Course proficiency percentages have already exceeded a 2014 baseline. This suggests that CTLS played a central role in CCSD’s successful recovery after the COVID-19 pandemic.
The overall response to the digital learning initiative from the end users that it is intended to serve has also been overwhelmingly positive. The initiative is now well-positioned to broaden learning opportunities across all schools and improve communication with parents and other stakeholders. CCSD’s experience in scaling CTLS offers useful lessons for districts that are ready to launch and to own their transformative digital learning environment.
We provide evidence that graduated driver licensing (GDL) laws, originally intended to improve public safety, impact human capital accumulation. Many teens use automobiles to access both school and employment. Because school and work decisions are interrelated, the effects of automobile-specific mobility restrictions are ambiguous. Using a novel triple-difference research design, we find that restricting mobility significantly reduces high school dropout rates and teen employment. We develop a multiple discrete choice model that rationalizes unintended consequences and reveals that school and work are weak complements. Thus, improved educational outcomes reflect decreased access to leisure activities rather than reduced labor market access.
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.
As affirmative action loses political feasibility, many universities have implemented race-neutral alternatives like top percent policies and holistic review to increase enrollment among disadvantaged students. I study these policies’ application, admission, and enrollment effects using University of California administrative data. UC’s affirmative action and top percent policies increased underrepresented minority (URM) enrollment by over 20 percent and less than 4 percent, respectively. Holistic review increases implementing campuses’ URM enrollment by about 7 percent. Top percent policies and holistic review have negligible effects on lower-income enrollment, while race-based affirmative action modestly increased enrollment among very low-income students. These findings highlight the enrollment gaps between affirmative action and its most common race-neutral alternatives and reveal that available policies do not substantially affect universities’ socioeconomic composition.
Faced with decreasing funds and increasing costs, a growing number of school districts across the United States are switching to four-day school weeks (4DSWs). Although previously used only by rural districts, the policy has begun to gain traction in metropolitan districts. We examine homeowner, teacher, and student outcomes in one of the first metropolitan school districts to adopt the 4DSW. We find 2 to 4 percent home price declines relative to surrounding school districts, a 5 percent decrease in teacher retention for experienced teachers, and a 0.2 to 0.3 standard deviation decrease in student test scores. These results suggest the decision to adopt a 4DSW in a metropolitan setting should not be taken lightly.
Current public pension funding policy has arguably failed on both theoretical and empirical grounds. The traditional actuarial approach elides the risk-return tradeoff at the heart of finance economics and has resulted in steadily rising contribution rates, instead of a sustainable steady state. We propose an economic reformulation of funding policy integrating: (1) steady-state determination of the expected contribution rate, based on an expected return on risky assets and a target funded ratio based on a low-risk discount rate for liabilities; (2) adjustment parameters to achieve convergence toward steady state; and (3) determination of target funded ratio based on policymakers’ revealed preference toward risk, by their choice of asset allocation under a simplified objective function. This provides a new understanding of the basis for pre-funding, in which the perceived net benefits of risky investment may far outweigh the traditional Samuelsonian rationale. Specifically, we find that convexity of the long-run risk-return relationship should lead more risk-tolerant policymakers to pursue higher target funded ratios. We believe our analysis provides the basis for reformulating contribution policy in a way that better supports sustainability and more coherently conveys the tradeoffs consistent with finance economics, and as evaluated by policymakers.
We study the returns to experience in teaching, estimated using supervisor ratings from classroom observations. We describe the assumptions required to interpret changes in observation ratings over time as the causal effect of experience on performance. We compare two difference-in-differences strategies: the two-way fixed effects estimator common in the literature, and an alternative which avoids potential bias arising from effect heterogeneity. Using data from Tennessee and Washington, DC, we show empirical tests relevant to assessing the identifying assumptions and substantive threats—e.g., leniency bias, manipulation, changes in incentives or job assignments—and find our estimates are robust to several threats.
Despite the growing popularity of free college proposals, countries with higher college subsidies tend to have higher enrollment rates but not higher graduation rates. To capture this evidence and evaluate potential free college policies, we rely on a dynamic model of college enrollment, performance, and graduation estimated using rich student-level data from Colombia. In the model, student effort affects class completion and mitigates the risk of performing poorly or dropping out. Among our simulated policies, universal free college expands enrollment the most but has virtually no effect on graduation rates, helping explain the cross-country evidence. Performance-based free college triggers a more modest enrollment expansion but delivers a higher graduation rate at a lower fiscal cost. While both programs lower student uncertainty relative to the baseline, performance-based free college does it to a lower extent, which in turn promotes better student outcomes. Overall, free college programs expand enrollment but have limited impacts on graduation and attainment due to their limited impact on student effort.
This paper estimates the heterogeneous labor market effects of enrolling in higher education short-cycle (SC) programs. Expanding access to these programs might affect the behavior of some students (compliers) in two margins: the expansion margin (students who would not have enrolled in higher education otherwise) and the diversion margin (students who would have enrolled in bachelor’s programs otherwise). To quantify these responses, we exploit local exogenous variation in the supply of higher education institutions (HEIs) facing Colombian high school graduates in an empirical multinomial choice model with several instruments. According to our findings, the presence of at least one HEI specialized in SC programs in the vicinity of the student’s high school municipality increases SC enrollment by 3.7-4.5 percentage points (40-50% of the SC enrollment rate). The diversion margin largely drives this effect. For female compliers, enrollment in SC programs increases formal employment relative to the next-best alternative. For male compliers, in contrast, it lowers formal employment and wages. These results should alert policymakers of the unexpected consequences of higher education expansionary policies.
Short-cycle higher education programs (SCPs), lasting two or three years, capture about a quarter of higher education enrollment in the world and can play a key role enhancing workforce skills. In this paper, we estimate the program-level contribution of SCPs to student academic and labor market outcomes, and study how and why these contributions vary across programs. We exploit unique administrative data from Colombia on the universe of students, institutions, and programs to control for a rich set of student, peer, and local choice set characteristics. We find that program-level contributions account for about 60-70 percent of the variation in student-level graduation and labor market outcomes. Our estimates show that programs vary greatly in their contributions, across and especially within fields of study. Moreover, the estimated contributions are strongly correlated with program outcomes but not with other commonly used quality measures. Programs contribute more to formal employment and wages when they are longer, have been provided for a longer time, are taught by more specialized institutions, and are offered in larger cities.