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Long school bus rides adversely affect student performance and well-being. Rural school bus rides are particularly long, incentivizing parents to drive their children to school rather than to opt for the school bus. This in turn exacerbates the traffic congestion around schools, further compounding the problem of long bus rides, creating a vicious cycle. It also results in underutilized school buses and higher bus operating costs per rider. To address these challenges, this paper focuses on the design of rural school bus routes and schedules, a particularly challenging problem due to its unique operational complexities, including mixed loading and irregular road networks. We formalize a rural school bus routing and scheduling model that tackles these complexities while minimizing the total bus ride time of students. We develop an original road network-aware cluster-then-route heuristic that leverages our problem formulation to produce high-quality solutions. For real-world case studies, our approach outperforms status quo solutions by reducing the bus ride times of students by 37-39 %. Our solutions also make the school bus more attractive, helping address both the underutilization

Using data on shootings in U.S.\ K--12 schools from 1981 to 2022, we estimate the effect of temperature on school shootings and assess climate-change impacts. We find that days with maximum temperatures above 90$^{\circ}$F increase school shooting incidence by approximately 90\% relative to days with maximum temperatures below 70$^{\circ}$F. The response is concentrated in interpersonal incidents and in non-class periods, such as before school, dismissal, after school, and lunch: shootings during these periods more than triple on days with maximum temperatures above 90$^{\circ}$F, while shootings during class time show no detectable temperature response. The estimated effects are positive for both indoor and outdoor shootings and are larger for shootings involving fatalities or injuries than for shootings involving only minor or no injuries. Applying the estimated dose-response to future warming, we estimate that interpersonal school shootings increase by 6\% by mid-century (2051--2060) under moderate emissions (SSP2--4.5) and 8\% under high emissions (SSP5--8.5), or about 12 and 16 additional incidents per decade. The present discounted value of mid-century social costs is \$599 m

This paper proposes a novel school choice system where schools are grouped into hierarchical bundles and offered to students as options for preference reports. By listing a bundle, a student seeks admission to any school within it without ranking the schools. This approach helps students who struggle to rank schools precisely and expands options on limited preference lists, potentially improving match outcomes. We design a modified deferred acceptance mechanism to handle bundle reports while preserving stability. Two laboratory experiments support our theory, showing that well-constructed bundles aligned with student preferences enhance welfare and match rates without compromising fairness. Practical applications are discussed.

Most US school districts draw geographic "attendance zones" to assign children to schools based on their home address, a process that can replicate existing neighborhood racial/ethnic and socioeconomic status (SES) segregation in schools. Redrawing boundaries can reduce segregation, but estimating expected rezoning impacts is often challenging because families can opt-out of their assigned schools. This paper seeks to alleviate this societal problem by developing a joint redistricting and choice modeling framework, called Redistricting with Choices (RWC). The RWC framework is applied to a large US public school district to estimate how redrawing elementary school boundaries might realistically impact levels of socioeconomic segregation. The main methodological contribution of RWC is a contextual stochastic optimization model that aims to minimize district-wide segregation by integrating rezoning constraints with a machine learning-based school choice model. The study finds that RWC yields boundary changes that might reduce segregation by a substantial amount (23%) -- but doing so might require the re-assignment of a large number of students, likely to mitigate re-segregation that c

Improving global school connectivity is critical for ensuring inclusive and equitable quality education. To reliably estimate the cost of connecting schools, governments and connectivity providers require complete and accurate school location data - a resource that is often scarce in many low- and middle-income countries. To address this challenge, we propose a cost-effective, scalable approach to locating schools in high-resolution satellite images using weakly supervised deep learning techniques. Our best models, which combine vision transformers and convolutional neural networks, achieve AUPRC values above 0.96 across 10 pilot African countries. Leveraging explainable AI techniques, our approach can approximate the precise geographical coordinates of the school locations using only low-cost, classification-level annotations. To demonstrate the scalability of our method, we generate nationwide maps of school location predictions in African countries and present a detailed analysis of our results, using Senegal as our case study. Finally, we demonstrate the immediate usability of our work by introducing an interactive web mapping tool to streamline human-in-the-loop model validati

In school choice, policymakers consolidate a district's objectives for a school into a priority ordering over students. They then face a trade-off between respecting these priorities and assigning students to more-preferred schools. However, because priorities are the amalgamation of multiple policy goals, some may be more flexible than others. This paper introduces a model that distinguishes between two types of priority: a between-group priority that ranks groups of students and must be respected, and a within-group priority for efficiently allocating seats within each group. The solution I introduce, the unified core, integrates both types. I provide a two-stage algorithm, the DA-TTC, that implements the unified core and generalizes both the Deferred Acceptance and Top Trading Cycles algorithms. This approach provides a method for improving efficiency in school choice while honoring policymakers' objectives.

This study discusses the opportunity to integrate tinkering, a constructionist practice, into formal education, highlighting its potential and challenges. We propose a model through which teachers can blend the open exploratory nature of tinkering with structured learning in primary school classrooms, focusing on Physics Education. Despite pandemic-induced limitations, feedback from 20 teachers and analysis of fishbowl protocols revealed the positive impact of tinkering on classroom dynamics, teacher engagement, and student access to knowledge. Our findings indicated that tinkering can surface relevant scientific questions. Nevertheless, teachers feel unprepared to tackle them in the classroom. This evidence will guide our future co-designs to enhance learning experiences and address the complexities of incorporating tinkering into formal education.

Do school openings trigger Covid-19 diffusion when school-age vaccination is available? We investigate this question using a unique geo-referenced high frequency database on school openings, vaccinations, and Covid-19 cases from the Italian region of Sicily. The analysis focuses on the change of Covid-19 diffusion after school opening in a homogeneous geographical territory. The identification of causal effects derives from a comparison of the change in cases before and after school opening in 2020/21, when vaccination was not available, and in 2021/22, when the vaccination campaign targeted individuals of age 12-19 and above 19. The results indicate that, while school opening determined an increase in the growth rate of Covid-19 cases in 2020/2021, this effect has been substantially reduced by school-age vaccination in 2021/2022. In particular, we find that an increase of approximately 10% in the vaccination rate of school-age population reduces the growth rate of Covid-19 cases after school opening by approximately 1.4%. In addition, a counterfactual simulation suggests that a permanent no vaccination scenario would have implied an increase of 19% in ICU beds occupancy.

In the UK, US and elsewhere, school accountability systems increasingly compare schools using value-added measures of school performance derived from pupil scores in high-stakes standardised tests. Rather than naively comparing school average scores, which largely reflect school intake differences in prior attainment, these measures attempt to compare the average progress or improvement pupils make during a year or phase of schooling. Schools, however, also differ in terms of their pupil demographic and socioeconomic characteristics and these also predict why some schools subsequently score higher than others. Many therefore argue that value-added measures unadjusted for pupil background are biased in favour of schools with more 'educationally advantaged' intakes. But, others worry that adjusting for pupil background entrenches socioeconomic inequities and excuses low performing schools. In this article we explore these theoretical arguments and their practical importance in the context of the 'Progress 8' secondary school accountability system in England which has chosen to ignore pupil background. We reveal how the reported low or high performance of many schools changes dramatic

Artificial intelligence (AI) has become a transformative force across global societies, reshaping the ways we communicate, collaborate, and make decisions. Yet, as AI systems increasingly mediate interactions between humans, questions about the ability to take into account and understand culture, language, and context have taken center stage. This book explores these questions through a series of articles that try to assess AI's capacity to navigate cross-cultural, multilingual, and high-stakes policy environments, emphasizing human-centered approaches that balance technological innovation with social equity. It brings together six case studies from the First African Digital Humanism Summer School that took place in Kigali, Rwanda in July 2025.

We examine Illinois educational data from standardized exams and analyze primary factors affecting the achievement of public school students. We focus on the simplest possible models: representation of data through visualizations and regressions on single variables. Exam scores are shown to depend on school type, location, and poverty concentration. For most schools in Illinois, student test scores decline linearly with poverty concentration. However Chicago must be treated separately. Selective schools in Chicago, as well as some traditional and charter schools, deviate from this pattern based on poverty. For any poverty level, Chicago schools perform better than those in the rest of Illinois. Selective programs for gifted students show high performance at each grade level, most notably at the high school level, when compared to other Illinois school types. The case of Chicago charter schools is more complex. In the last six years, their students' scores overtook those of students in traditional Chicago high schools.

Education systems around the world increasingly rely on school value-added models to hold schools to account. These models typically focus on a limited number of academic outcomes, failing to recognise the broader range of non-academic student outcomes, attitudes and behaviours to which schools contribute. We explore how the traditional multilevel modelling approach to school value-added models can be extended to simultaneously analyse multiple academic and non-academic outcomes and thereby can potentially provide a more rounded approach to using student data to inform school accountability. We jointly model student attainment, absence and exclusion data for schools in England. We find different results across the three outcomes, in terms of the size and consistency of school effects, and the importance of adjusting for student and school characteristics. The results suggest the three outcomes are capturing fundamentally distinct aspects of school performance, recommending the consideration of non-academic outcomes in systems of school accountability.

This study considers a model where schools may have multiple priority orders on students, which may be inconsistent with each other. For example, in school choice systems, since the sibling priority and the walk zone priority coexist, the priority orders based on them would be conflicting. We introduce a weaker fairness notion called M-fairness to examine such markets. Further, we focus on a more specific situation where all schools have only two priority orders, and for a certain group of students, a priority order of each school is an improvement of the other priority order of the school. An illustrative example is the school choice matching market with a priority-based affirmative action policy. We introduce a mechanism that utilizes the efficiency adjusted deferred acceptance algorithm and show that the mechanism satisfies properties called responsiveness to improvements and improved-group optimally M-stability, which is stronger than student optimally M-stability.

This paper introduces a novel benchmark dataset designed to evaluate the capabilities of Vision Language Models (VLMs) on tasks that combine visual reasoning with subject-specific background knowledge in the German language. In contrast to widely used English-language benchmarks that often rely on artificially difficult or decontextualized problems, this dataset draws from real middle school curricula across nine domains including mathematics, history, biology, and religion. The benchmark includes over 2,000 open-ended questions grounded in 486 images, ensuring that models must integrate visual interpretation with factual reasoning rather than rely on superficial textual cues. We evaluate thirteen state-of-the-art open-weight VLMs across multiple dimensions, including domain-specific accuracy and performance on adversarial crafted questions. Our findings reveal that even the strongest models achieve less than 45% overall accuracy, with particularly poor performance in music, mathematics, and adversarial settings. Furthermore, the results indicate significant discrepancies between success on popular benchmarks and real-world multimodal understanding. We conclude that middle school-l

Ongoing school closures and gradual reopenings have been occurring since the beginning of the COVID-19 pandemic. One substantial cost of school closure is breakdown in channels of reporting of violence against children, in which schools play a considerable role. There is, however, little evidence documenting how widespread such a breakdown in reporting of violence against children has been, and scant evidence exists about potential recovery in reporting as schools re-open. We study all formal criminal reports of violence against children occurring in Chile up to December 2021, covering physical, psychological, and sexual violence. This is combined with administrative records of school re-opening, attendance, and epidemiological and public health measures. We observe sharp declines in violence reporting at the moment of school closure across all classes of violence studied. Estimated reporting declines range from -17% (rape), to -43% (sexual abuse). While reports rise with school re-opening, recovery of reporting rates is slow. Conservative projections suggest that reporting gaps remained into the final quarter of 2021, nearly two years after initial school closures. Our estimates s

In 2018, in response to the proposed elimination of physics at a predominately Hispanic and socioeconomically disadvantaged (SED) high school, the Northern California/Nevada chapter of the AAPT investigated school demographics and their effect on physics offerings in public high schools in our region. As access was a key issue, the focus was on public, non-charter high schools, which are free to students and do not require winning a lottery for attendance. As reported previously, the data revealed that the percentage of Hispanic students and the percentage of SED students at a high school are highly correlated (r^2=0.60). Additionally, these factors could be used as predictors of a school's physics offerings. To determine if the disparities in course offerings extended through other Advanced Placement (AP) STEM classes the data was further analyzed, revealing that as the popularity of an AP exam drops, so do the relative odds of it being offered, when comparing schools with different demographics. A Northern California public high school student is much more likely to get a strong selection of AP STEM classes if their school serves an affluent, non-Hispanic student majority rather

The African School of Fundamental Physics and Applications is a biennial school in Africa. It is based on the observation that fundamental physics provides excellent motivation for students of science. The aim of the school is to build capacity to harvest, interpret, and exploit the results of current and future physics experiments and to increase proficiency in related applications. The participating students are selected from all over Africa. The school also offers a workshop to train high school teachers, an outreach to motivate high school pupils and a physics conference to support a broader participation of African research faculties. Support for the school comes from institutes in Africa, Europe, USA and Asia. In this paper, we will present the school and discuss strategies to make the school sustainable.

The Brazilian Mathematical Olympiads for Public Schools (OBMEP) is held every year since 2005. In the 2013 edition there were over 47,000 schools registered involving nearly 19.2 million students. The Brazilian public educational system is structured into three administrative levels: federal, state and municipal. Students participating in the OBMEP come from three educational levels, two in primary and one in secondary school. We aim at studying the performance of Brazilian public schools which have been taking part of the OBMEP from 2006 until 2013. We propose a standardization of the mean scores of schools per year and educational level which is modeled through a hierarchical dynamic beta regression model. Both the mean and precision of the beta distribution are modeled as a function of covariates whose effects evolve smoothly with time. Results show that, regardless of the educational level, federal schools have better performance than municipal or state schools. The mean performance of schools increases with the human development index (HDI) of the municipality the school is located in. Moreover, the difference in mean performance between federal and state or municipal schools

Several countries successfully use centralized matching schemes for school or higher education assignment, or for entry-level labour markets. In this paper we explore the computational aspects of a possible similar scheme for assigning teachers to schools. Our model is motivated by a particular characteristic of the education system in many countries where each teacher specializes in two subjects. We seek stable matchings, which ensure that no teacher and school have the incentive to deviate from their assignments. Indeed we propose two stability definitions depending on the precise format of schools' preferences. If the schools' ranking of applicants is independent of their subjects of specialism, we show that the problem of deciding whether a stable matching exists is NP-complete, even if there are only three subjects, unless there are master lists of applicants or of schools. By contrast, if the schools may order applicants differently in each of their specialization subjects, the problem of deciding whether a stable matching exists is NP-complete even in the presence of subject-specific master lists plus a master list of schools. Finally, we prove a strong inapproximability res