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Principals shape the academic setting of schools. Yet, there is limited evidence on whether principal professional development improves schooling outcomes. Beginning in 2008-09, Pennsylvania's Inspired Leadership (PIL) induction program required that newly hired principals complete targeted in-service professional development tied to newly established state leadership standards within five years of employment. Using panel data on all Pennsylvania students, teachers, and principals, we leverage variation in the timing of PIL induction across principal-school cells and employ difference-in-differences and event study strategies to estimate the impact of PIL induction on teacher and student outcomes. We find that PIL induction increased student math achievement through improvements in teacher effectiveness, and that the effects of PIL induction on teacher effectiveness were concentrated among the most economically disadvantaged and urban schools in Pennsylvania. Principal professional development had the greatest impact on teacher effectiveness when principals completed PIL induction during their first two years in the principalship. We also find evidence that teacher turnover declined in the years following the completion of PIL induction. We discuss the implications of our findings for principal induction efforts.

The paper proposes a time-varying parameter global vector autoregressive (TVP-GVAR) framework for predicting and analysing developed region economic variables. We want to provide an easily accessible approach for the economy application settings, where a variety of machine learning models can be incorporated for out-of-sample prediction. The LASSO-type technique for numerically efficient model selection of mean squared errors (MSEs) is selected. We show the convincing in-sample performance of our proposed model in all economic variables and relatively high precision out-of-sample predictions with different-frequency economic inputs. Furthermore, the time-varying orthogonal impulse responses provide novel insights into the connectedness of economic variables at critical time points across developed regions. We also derive the corresponding asymptotic bands (the confidence intervals) for orthogonal impulse responses function under standard assumptions.

This report examines the relationship between students' participation in high-impact practices (HIP) and their perceived learning gains, with special attention paid to the nature of these relationships among Black and Hispanic students and among students 25 years or older. In 2018, with support from Lumina Foundation, the National Association of System Heads (NASH) launched an effort with four public college and university systems to demonstrate how a coordinated approach could expand and improve quality implementation of high-impact practices in an equitable manner. Drawing on National Survey of Student Engagement (NSSE) data involving more than 28,000 students from 15 institutions across three states participating in the Lumina-NASH initiative, this report assesses the benefits associated with participation in various HIPs in terms of students' academic and practical learning. Overall, analyses point to a substantial boost in perceived learning gains for students participating in HIPs, with an especially large benefit associated with participating in service-learning courses involving a community-based project. Participation in HIPs is strongly associated with learning gains for Black and Hispanic students as well as for students 25 years and older. However, analyses suggest that these relationships vary among the different HIPs measured by NSSE.

Study after study has found that new teachers tend to be less effective than educators with more experience. But despite having more junior staff, charter networks (referred to as CMOs) often outperform their district peers. So what's their secret? To find out, this study explores how teacher effectiveness varies and evolves across traditional and charter public schools, as well as within the sector's CMOs and standalone schools. Using data collected by the Pennsylvania Department of Education between 2007 and 2017, George Mason University associate professor Matthew Steinberg and University of Pennsylvania doctoral student Haisheng Yang examine the impact that over 40,000 teachers in charter and traditional public schools had on students' math and English language arts (ELA) achievement in grades three through eight. Among the report's key findings: (1) On average, teachers in Pennsylvania charter schools are more effective in ELA but less effective in math than teachers in traditional public schools. However, teachers in CMO-run schools are more effective in "both" subjects; (2) On average, teachers in Pennsylvania's CMOs improve more rapidly than teachers in traditional public schools or standalone charters--especially when teaching math; and (3) Unlike Pennsylvania's traditional public schools, the state's CMOs promote many of their most effective teachers into leadership roles. As the authors note, these findings suggest that Pennsylvania's CMOs are succeeding with "a fundamentally different approach to human capital," which relies heavily on their ability to help novice teachers improve more quickly than their counterparts in traditional public schools. But of course, it's critical that schools in both sectors do what they can to help new teachers get better faster. In short, accelerating teacher improvement is yet another area where all schools could learn from the success of our nation's most effective charter school networks. [Foreword by Amber M. Northern and Michael J. Petrilli.]

Public school districts in large cities like Philadelphia are especially challenged to provide every school with an effective school leader. Principal mobility--that is, transferring from one school to another or leaving the principalship entirely--is disproportionately concentrated in school districts serving more low-income students and has negative consequences for student performance. For these reasons, it is critical for policymakers and school leaders in Philadelphia to have a clear picture of the extent and nature of principal mobility. This report provides evidence on principal mobility among traditional public schools (TPS) in the School District of Philadelphia (SDP) and charter public schools located in Philadelphia during the 2007-08 through 2015-16 school years. The study uses individual-level data on all principals in traditional and charter schools provided by the Pennsylvania Department of Education. The authors describe the characteristics of principals in Philadelphia, the extent of principal mobility, and the characteristics of mobile principals and the schools that they exit. For this report, principal mobility is defined as occurring when a traditional or charter school principal does not return to the same school the following year. By this definition, a mobile principal is one who moved to another school in Philadelphia (either a traditional or charter school), moved to a Pennsylvania public school outside of Philadelphia, exited the principalship (i.e., was employed in another role in education), or left public education in Pennsylvania.

We present a new particle image correlation technique for resolving nanoparticle flow velocity using confocal laser scanning microscopy (CLSM). The two primary issues that complicate nanoparticle scanning laser image correlation (SLIC) based velocimetry are (1) the use of diffusion dominated nanoparticles as flow tracers, which introduce a random decorrelating error into the velocity estimate, and (2) the effects of the scanning laser image acquisition, which introduces a bias error. To date, no study has quantified these errors or demonstrated a means to deal with them in SLIC velocimetry. In this work, we build upon the robust phase correlation (RPC) and existing methods of SLIC to quantify and mitigate these errors. First, we implement an ensemble RPC instead of using an ensemble standard cross correlation, and develop an SLIC optimal filter that maximizes the correlation strength in order to reliably and accurately detect the correlation peak representing the most probable average displacement of the nanoparticles. Secondly, we developed an analytical model of the SLIC measurement bias error due to image scanning of diffusion dominated tracer particles. We show that the bias error depends only on the ratio of the mean velocity of the tracer particles to that of the laser scanner and we use this model to correct the induced errors. We validated our technique using synthetic images and experimentally obtained SLIC images of nanoparticle flow through a micro-channel. Our technique reduced the error by up to a factor of ten compared to other SLIC algorithms for the images tested in this study. Moreover, our optimized RPC filter is reducing the number of image pairs required for the convergence of the ensemble correlation by two orders of magnitude compared to the standard cross correlation., Comment: 30 pages, 13 figures, 2 tables, submitted to Measurement Science and Technology - Special Issue Article (PIV 2015)