Faculty and Staff

Does school participatory budgeting (SPB) increase students’ political efficacy? SPB, which is implemented in thousands of schools around the world, is a democratic process of deliberation and decision-making in which students determine how to spend a portion of the school’s budget. We examined the impact of SPB on political efficacy in one middle school in Arizona. Our participants’ (n = 28) responses on survey items designed to measure self-perceived growth in political efficacy indicated a large effect size (Cohen’s d = 1.46), suggesting that SPB is an effective approach to civic pedagogy, with promising prospects for developing students’ political efficacy.

The recent battle reported from Washington about proposed national testing program does not tell the most important political story about high stakes tests. Politically popular school accountability systems in many states already revolve around statistical results of testing with high-stakes environments. The future of high stakes tests thus does not depend on what happens on Capitol Hill. Rather, the existence of tests depends largely on the political culture of published test results. Most critics of high-stakes testing do not talk about that culture, however. They typically focus on the practice legacy of testing, the ways in which testing creates perverse incentives against good teaching.

More important may be the political legacy, or how testing defines legitimate discussion about school politics. The consequence of statistical accountability systems will be the narrowing of purpose for schools, impatience with reform, and the continuing erosion of political support for publicly funded schools. Dissent from the high-stakes accountability regime that has developed around standardized testing, including proposals for professionalism and performance assessment, commonly fails to consider these political legacies. Alternatives to standardized testing which do not also connect schooling with the public at large will not be politically viable.

Accessibility is increasingly used as a metric when evaluating changes to public transport systems. Transit travel times contain variation depending on when one departs relative to when a transit vehicle arrives, and how well transfers are coordinated given a particular timetable. In addition, there is necessarily uncertainty in the value of the accessibility metric during sketch planning processes, due to scenarios which are underspecified because detailed schedule information is not yet available. This article presents a method to extend the concept of "reliable" accessibility to transit to address the first issue, and create confidence intervals and hypothesis tests to address the second.

There is a need for indicators of transportation-land use system quality that are understandable to a wide range of stakeholders, and which can provide immediate feedback on the quality of interactively designed scenarios. Location-based accessibility indicators are promising candidates, but indicator values can vary strongly depending on time of day and transfer wait times. Capturing this variation increases complexity, slowing down calculations. We present new methods for rapid yet rigorous computation of accessibility metrics, allowing immediate feedback during early-stage transit planning, while being rigorous enough for final analyses. Our approach is statistical, characterizing the uncertainty and variability in accessibility metrics due to differences in departure time and headway-based scenario specification. The analysis is carried out on a detailed multi-modal network model including both public transportation and streets. Land use data are represented at high resolution. These methods have been implemented as open-source software running on commodity cloud infrastructure. Networks are constructed from standard open data sources, and scenarios are built in a map-based web interface. We conclude with a case study, describing how these methods were applied in a long-term transportation planning process for metropolitan Amsterdam.