A New Wave of School Integration: Districts and Charters Pursuing Socioeconomic Diversity

Students in racially and socioeconomically integrated schools experience academic, cognitive, and social benefits that are not available to students in racially isolated, high-poverty environments. A large body of research going back five decades underscores the improved experiences that integrated schools provide. And yet, more than sixty years after Brown v. Board of Education, American public schools are still highly segregated by both race and class. In fact, by most measures of integration, our public schools are worse off, since they are now even more racially segregated than they were in the 1970s, and economic segregation in schools has risen dramatically over the past two decades.In this report, we highlight the work that school districts and charter schools across the country are doing to promote socioeconomic and racial integration by considering socioeconomic factors in student assignment policies.Key findings of this report include:Our research has identified a total of 91 districts and charter networks across the country that use socioeconomic status as a factor in student assignment. The 91 school districts and charter schools with socioeconomic integration policies enroll over 4 million students. The school districts and charter networks identified as employing socioeconomic integration are located in 32 different states. The majority of districts and charters on the list have racially and socioeconomically diverse enrollments. The majority of the integration strategies observed fall into five main categories: attendance zone boundaries, district-wide choice policies, magnet school admissions, charter school admissions, and transfer policies.The push toward socioeconomic and racial integration is perhaps the most important challenge facing American public schools. Segregation impedes the ability of children to prepare for an increasingly diverse workforce; to function tolerantly and enthusiastically in a globalizing society; to lead, follow, and communicate with a wide variety of consumers, colleagues, and friends. The democratic principles of this nation are impossible to reach without universal access to a diverse, high quality, and engaging education

Semantic Stability in Social Tagging Streams

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language streams alone

Moving At-Risk Teenagers Out of High-Risk Neighborhoods: Why Girls Fare Better Than Boys

neighborhood effects; social experiment; mixed methods; youth risk behavior

Geographic constraints on social network groups

Social groups are fundamental building blocks of human societies. While our social interactions have always been constrained by geography, it has been impossible, due to practical difficulties, to evaluate the nature of this restriction on social group structure. We construct a social network of individuals whose most frequent geographical locations are also known. We also classify the individuals into groups according to a community detection algorithm. We study the variation of geographical span for social groups of varying sizes, and explore the relationship between topological positions and geographic positions of their members. We find that small social groups are geographically very tight, but become much more clumped when the group size exceeds about 30 members. Also, we find no correlation between the topological positions and geographic positions of individuals within network communities. These results suggest that spreading processes face distinct structural and spatial constraints.Comment: 10 pages, 5 figure

Hierarchical relational models for document networks

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Networked Living: a new approach to teaching introductory ICT

The course T175 Networked Living is a 300 hour, multiple media, distance learning course offered by the UK Open University. The first presentation of the course, in 2005, attracted over 1600 students. T175 introduces students to general concepts of information and communication technology in a range of contexts, including: communication and identity; entertainment and information; and health, transport and government. It is an introductory (level 1) course for a variety of bachelors’ degrees, including the BSc programmes in: Information and Communication Technology; IT and Computing; and Technology; as well as the BEng engineering programme. The course was designed with a focus on retention of students and preparing them for further study. Student workload and pacing was carefully planned and there is a significant study skills component. The course uses a range of media, including: text, audio, computer animation and other software, and a website. Active learning is encouraged by means of activities, online quizzes, animations, spreadsheets and a learning journal. Continuous assessment is carried out via a mix of multiple-choice assignments (to test factual and numerical skills) and written assignments (which include elementary research into new topics). The course culminates with a written end-of-course assessment. This includes a major reflective component, as well as more traditional questions designed to test knowledge and understanding

A generative model for feedback networks

We investigate a simple generative model for network formation. The model is designed to describe the growth of networks of kinship, trading, corporate alliances, or autocatalytic chemical reactions, where feedback is an essential element of network growth. The underlying graphs in these situations grow via a competition between cycle formation and node addition. After choosing a given node, a search is made for another node at a suitable distance. If such a node is found, a link is added connecting this to the original node, and increasing the number of cycles in the graph; if such a node cannot be found, a new node is added, which is linked to the original node. We simulate this algorithm and find that we cannot reject the hypothesis that the empirical degree distribution is a q-exponential function, which has been used to model long-range processes in nonequilibrium statistical mechanics.Comment: 11 pages, 6 figure