Anthony S. Bryk – författare

A comprehensive analysis of the astonishing changes that elevated the Chicago public school system from one of the worst in the nation to one of the most improved. How a City Learned to Improve Its Schools tells the story of the extraordinary thirty-year school reform effort that changed the landscape of public education in Chicago. Acclaimed educational researcher Anthony S. Bryk joins five coauthors involved in Chicago-based education reform, Sharon Greenberg, Albert Bertani, Penny Sebring, Steven E. Tozer, and Timothy Knowles, to illuminate the many factors that led to the ultimate success of Chicago Public Schools.Beginning in 1987, Bryk and team lay out the civic context for reform, outlining the systemic challenges such as segregation, institutional racism, and income and resource disparities that reformers grappled with as well as the social conflicts they faced. Next, they analyze continuous improvement to teaching and learning, including the recruitment and retention of high-quality educators and leadership, a transformation validated by unprecedented increases in benchmarks such as graduation rates and college matriculation. This riveting account introduces key actors within the schools, city government, and business community, and the partnerships they forged. It also reveals the surprising yet essential role of Chicago's innovative information infrastructure in aligning disparate initiatives.In making clear how elements such as advocacy, expanded civic capacity, improvement research, and strong democracy contributed to large-scale progress in the system's 600-plus schools, the book highlights the greater lessons that the Chicago story offers for system improvement overall.

In 1988 the Chicago public school system decentralized, granting parents and communities significant resources and authority to reform their schools in dramatic ways. To track the effects of this bold experiment, the authors of "Organizing Schools for Improvement" collected a wealth of data on elementary schools in Chicago. They identified one hundred elementary schools that had substantially improved, and one hundred that had not, over a seven-year period. What had the successful schools done to accelerate student learning? The authors of this illuminating book identify a comprehensive set of practices and conditions that were key factors for improvement, including school leadership, the professional capacity of the faculty and staff, and a student-centered learning climate. In addition, they analyze the impact of social dynamics, including crime, critically examining the inextricable link between schools and their communities. Putting their data onto a more human scale, they also chronicle the stories of two neighboring schools with very different trajectories. The lessons gleaned from this groundbreaking study will be invaluable for anyone involved with urban education.

The authors examine a broad range of Catholic high schools to determine whether or not students are better educated in these schools than they are in public schools. They find that the Catholic schools do have an independent effect on achievement, especially in reducing disparities between disadvantaged and privileged students. The Catholic school of today, they show, is informed by a vision, similar to that of John Dewey, of the school as a community committed to democratic education and the common good of all students.

"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high." --Short Book Reviews from the International Statistical Institute

"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research." --TED GERBER, Sociology, University of Arizona

"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."--PAUL SWANK, Houston School of Nursing, University of Texas, Houston

Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:

* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3* New section on multivariate growth models in Chapter 6 * A discussion of research synthesis or meta-analysis applications in Chapter 7* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators

While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:

* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case * New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model * New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)

The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.

As a field, education has largely failed to learn from experience. Time after time, promising education reforms fall short of their goals and are abandoned as other promising ideas take their place. In Learning to Improve, the authors argue for a new approach. Rather than “implementing fast and learning slow,” they believe educators should adopt a more rigorous approach to improvement that allows the field to “learn fast to implement well.”Using ideas borrowed from improvement science, the authors show how a process of disciplined inquiry can be combined with the use of networks to identify, adapt, and successfully scale up promising interventions in education. Organized around six core principles, the book shows how “networked improvement communities” can bring together researchers and practitioners to accelerate learning in key areas of education. Examples include efforts to address the high rate of failure among students in community college remedial math courses and strategies forimproving feedback to novice teachers.Learning to Improve offers a new paradigm for research and development in education that promises to be a powerful driver of improvement for the nation’s schools and colleges.