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Our fifth book in the International Research on School Leadership series focuses on the use of data in schools and districts as useful information for leadership and decision making. Schools are awash in data and information, from test scores, to grades, to discipline reports, and attendance as just a short list of student information sources, while additional streams of data feed into schools and districts from teachers and parents as well as local, regional and national policy levels. To deal with the data, schools have implemented a variety of data practices, from data rooms, to data days, data walks, and data protocols. However, despite the flood of data, successful school leaders are leveraging an analysis of their school’s data as a means to bring about continuous improvement in an effort to improve instruction for all students. Nevertheless, some drown, some swim, while others find success. Our goal in this book volume is to bring together a set of chapters by authors who examine successful data use as it relates to leadership and school improvement. In particular, the chapters in this volume consider important issues in this domain, including: • How educational leaders use data to inform their practice. • What types of data and data analysis are most useful to successful school leaders. • To what extent are data driven and data informed practices helping school leaders positively change instructional practice? • In what ways does good data collection and analysis feed into successful continuous improvement and holistic systems thinking? • How have school leadership practices changed as more data and data analysis techniques have become available? • What are the major obstacles facing school leaders when using data for decision making and how do they overcome them?
The recent passage of the Every Student Succeeds Act (ESSA) presents new opportunities and greater flexibility in efforts to personalize learning for all children. The Handbook on Personalized Learning for States, Districts, and Schools provides insight and guidance on maximizing that new flexibility. Produced by the Center on Innovations in Learning (CIL), one of seven national content centers funded by the U.S. Department of Education, this volume suggests how teachers can enhance personalized learning by cultivating relationships with students and their families to better understand a child’s learning and motivation. Personalized learning also encourages the development of students’ metacognitive, social, and emotional competencies, thereby fostering students’ self?direction in their own education, one aimed at mastery of knowledge and skills and readiness for career and college. Chapters address topics across the landscape of personalized learning, including co?designing instruction and learning pathways with students; variation in the time, place, and pace of learning, including flipped and blended classrooms; and using technology to manage and analyze the learning process. The Handbook’s chapters include Action Principles to guide states, districts, and schools in personalizing learning.
Data Analysis for Continuous School Improvement provides a new definition of school improvement, away from a singular focus on compliance, toward a true commitment to excellence. This book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of data. The updated edition provides a wealth of tools, protocols, timelines, examples, and strategies that will help schools and districts become genuine learning organizations.
Under a Congressional mandate, the National Center for Education Statistics (NCES) is required to collect data on the frequency, seriousness, and incidence of violence in elementary and secondary schools. The NCES responded to this requirement by commissioning a survey, the Principal/School Disciplinarian Survey on School Violence, the results of which are detailed in this report. The school violence survey was conducted with a nationally representative sample of 1,234 regular public elementary, middle, and secondary schools in the 50 states and the District of Columbia in the spring and summer of 1997. The survey requested information on: (1) the incidence of crime and violence in the public schools; (2) principals' (or school disciplinarians') perceptions about discipline issues; (3) types of disciplinary actions schools took; and (4) security and violence prevention measures in the schools. More than half of U.S. public schools reported experiencing at least one crime incident in the school year 1996-97, and 1 in 10 schools reported at least one serious violent crime during the school year. Crime and violence were more of a problem in middle and high schools than in elementary schools. Middle and high schools were more likely to report that they had experienced one or more incidents of any crime and one or more incidents of serious violent crime than elementary schools. Most public schools reported having zero tolerance policies towards serious student offenses, and most schools reported that they used low levels of security measures to prevent violence. Most schools reported having formal school violence prevention programs. An appendix contains the survey questionnaire. (Contains 12 figures, 32 tables.) (SLD)
In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of evidence, such as scores on students’ assessments, classroom observations etc. This book supports policy-makers, people working with schools, researchers and school leaders and teachers in the use of data, by bringing together the current research conducted on data use across multiple countries into a single volume. Some of these studies are ‘best practice’ studies, where effective data use has led to improvements in student learning. Others provide insight into challenges in both policy and practice environments. Each of them draws on research and literature in the field.
This book provides a comprehensive introduction to psychological assessment and covers areas not typically addressed in existing test and measurements texts, such as neuropsychological assessment and the use of tests in forensics settings. The book introduces the vocabulary of the profession and the most basic mathematics of testing early as being fundamental to understanding the field. Numerous examples are drawn from tests that the authors have written or otherwise helped to develop, reflecting the authors’ deep understanding of these tests and their familiarity with problems encountered in test development, use, and interpretation. Following the introduction of the basic areas of psychometrics, the book moves to areas of testing that represent various approaches to measuring different psychological constructs (memory, language, executive function, etc.), with emphasis on the complex issue of cultural bias in testing. Examples of existing tests are given throughout the book; however, this book is not designed to prepare students to go out and administer, score, and interpret specific psychological tests. Rather, the purpose of this book is to provide the foundational core of knowledge about tests, measurement, and assessment constructs, issues, and quantitative tools. Explains what constitutes a psychological test, how tests are developed, how they are best used, and how to evaluate their strengths and weaknesses; Describes areas of testing that represent different approaches to measuring different psychological constructs; Explains applications of psychological testing to issues in the courts; Addresses how test authors and publishers design and research tests to address the difficult and demanding issues of cultural differences in test performance and interpretation of test results.
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
Join us in celebrating the 25th anniversary of James A. Banks’ Multicultural Education Series, published by Teachers College Press—a dynamic series consisting of more than 70 published books with many more in the pipeline. This commemorative volume features engaging, incisive, and timely selections from the bestselling and most influential books in the series. Together, these selections address how multicultural education should be transformed for a nation and world that are becoming increasingly complex due to virulent racism, pernicious nationalism, mass migrations, interracial mixing, social-class stratification, and a global pandemic. Book Features: Informative and engaging selections from the most important and influential publications in the Multicultural Education Series. An introduction by James A. Banks that integrates and interrelates the chapters and describes how they can be used to transform multicultural education for a changing world. An afterword by Margaret Smith Crocco that synthesizes the book and describes ways to implement school reform that expands educational opportunity. Contributors: James A. Banks, Cherry A. McGee Banks, Margaret Smith Crocco, Linda Darling-Hammond, Robin DiAngelo, Paul C. Gorski, Tyrone C. Howard, Gary R. Howard, Carol D. Lee, James W. Loewen, Sonia Nieto, Pedro A. Noguera, Özlem Sensoy, Christine E. Sleeter, Esa Syeed, Guadalupe Valdés, Miguel Zavala
Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment!