University of Virginia

About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.

Recommendations for increasing replicability in psychology.

Abstract: Replicability of findings is at the heart of any empirical science. The aim of this article is to move the current replicability debate in psychology towards concrete recommendations for improvement. We focus on research practices but also offer guidelines for reviewers, editors, journal management, teachers, granting institutions, and university promotion committees, highlighting some of the emerging and existing practical solutions that can facilitate implementation of these recommendations. The challenges for improving replicability in psychological science are systemic. Improvement can occur only if changes are made at many levels of practice, evaluation, and reward. Copyright © 2013 John Wiley & Sons, Ltd.

Control Theory for Partial Differential Equations: Continuous and Approximation Theories

Abstract: Originally published in 2000, this is the second volume of a comprehensive two-volume treatment of quadratic optimal control theory for partial differential equations over a finite or infinite time horizon, and related differential (integral) and algebraic Riccati equations. Both continuous theory and numerical approximation theory are included. The authors use an abstract space, operator theoretic approach, which is based on semigroups methods, and which unifies across a few basic classes of evolution. The various abstract frameworks are motivated by, and ultimately directed to, partial differential equations with boundary/point control. Volume 2 is focused on the optimal control problem over a finite time interval for hyperbolic dynamical systems. A few abstract models are considered, each motivated by a particular canonical hyperbolic dynamics. It presents numerous fascinating results. These volumes will appeal to graduate students and researchers in pure and applied mathematics and theoretical engineering with an interest in optimal control problems.

Teacher-Student Relationships and Engagement: Conceptualizing, Measuring, and Improving the Capacity of Classroom Interactions

Abstract: Classrooms are complex social systems, and student-teacher relationships and interactions are also complex, multicomponent systems. We posit that the nature and quality of relationship interactions between teachers and students are fundamental to understanding student engagement, can be assessed through standardized observation methods, and can be changed by providing teachers knowledge about developmental processes relevant for classroom interactions and personalized feedback/support about their interactive behaviors and cues. When these supports are provided to teachers’ interactions, student engagement increases. In this chapter, we focus on the theoretical and empirical links between interactions and engagement and present an approach to intervention designed to increase the quality of such interactions and, in turn, increase student engagement and, ultimately, learning and development. Recognizing general principles of development in complex systems, a theory of the classroom as a setting for development, and a theory of change specific to this social setting are the ultimate goals of this work. Engagement, in this context, is both an outcome in its own right and a mediator of impacts that teachers have on student outcomes through their interactions with children and youth. In light of this discussion, we offer suggestions or directions for further research in this area.

Dynamic but Structural Equation Modeling of Repeated Measures Data

Abstract: The term “dynamic” is broadly defined as a pattern of change. Many scientists have searched for dynamics by calculating df/dt: the ratio of changes or differences d in a function f relative to changes in time t.This simple dynamic equation was used in the 16th and 17th century motion experiments of Galileo, in the 17th and 18th century gravitation experiments of Newton, and in the 19th century experiments of many physicists and chemists (see Morris, 1985). I also use this dynamic equation, but here I examine multivariate psychological change data using the 20th century developments of latent variable structural equation modeling.