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Educators are increasingly responsible for using data to improve teaching and learning in their schools. This helpful guide provides leaders with simple steps for facilitating accurate analysis and interpretation of data, while avoiding common errors and pitfalls. How to Make Data Work provides clear strategies for getting data into workable shape and creating an environment that supports understanding, analysis, and successful use of data, no matter what data system or educational technology tools are in place in your district. This accessible resource makes data easy to understand and use so that educators can better evaluate and maximize their systems to help their staff, students, and school succeed. With this tried-and-true guidance, you’ll be prepared to advocate for tools that adhere to data reporting standards, avoid misinterpretation of data, and improve the data use climate in your school.
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
Lots of pundits and researchers have ideas about how education data work should be done and who should do it. Many of their ideas are disseminated widely. This book covers the things those books do not: metadata, working with enterprise IT, data requests, politics, and why descriptive statistics can take you far.
The K-12 Educator’s Data Guidebook is a comprehensive field guide for school professionals learning to use data. “Non-data people,” rejoice! Requiring no prior proficiency in data tools and programming, this book validates the implicit challenges of learning to use data to empower educators and features original real-world examples from in-service educators to illustrate common problem-solving. Each chapter uses stories, humor, and a human approach to set the tone for a safe and fun learning experience. Through this highly practical foundation, everyday educators can better engage school initiatives, professional development, and instructional challenges that require competent data use for improving school systems.
Radically reimagine our ways of being, learning, and doing Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on "fixing" and "filling" academic gaps, we must envision and rebuild the system from the student up—with classrooms, schools and systems built around students’ brilliance, cultural wealth, and intellectual potential. Street data reminds us that what is measurable is not the same as what is valuable and that data can be humanizing, liberatory and healing. By breaking down street data fundamentals: what it is, how to gather it, and how it can complement other forms of data to guide a school or district’s equity journey, Safir and Dugan offer an actionable framework for school transformation. Written for educators and policymakers, this book · Offers fresh ideas and innovative tools to apply immediately · Provides an asset-based model to help educators look for what’s right in our students and communities instead of seeking what’s wrong · Explores a different application of data, from its capacity to help us diagnose root causes of inequity, to its potential to transform learning, and its power to reshape adult culture Now is the time to take an antiracist stance, interrogate our assumptions about knowledge, measurement, and what really matters when it comes to educating young people.
This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student's academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.
Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making. Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.
Praise for the First Edition: "Creighton′s descriptions of the applicability and helpfulness of statistics for educators is especially strong. His use of plain language and school-based examples is crucial." -Scott McLeod, Assistant Professor University of Cincinnati Improve instructional leadership practice with proven, easy-to-understand strategies for data-based decision making! Since the first edition of Schools and Data, the No Child Left Behind Act has swept the country, and data-based decision making is no longer an option for educators. Today′s educational climate makes it imperative for all schools to collect data and use statistical analysis to help create clear goals and recognize strategies for improvement. Schools and Data, Second Edition, focuses on the relevance of statistics in the lives of principals and teachers. Real-life examples and step-by-step procedures for collecting and organizing data provide every school leader with the means to facilitate more appropriate and effective decision making. With a highly practical method for statistical analysis, this comprehensive yet reader-friendly new edition places special emphasis on: Connecting statistics and educators′ daily work Integrating applied technology, including Excel and SPSS software Strengthening educators′ data interpretation skills Building strong skills in problem analysis, program evaluation, data-driven decision making, and report preparation Increasing the focus on correlation and regression Packed with easy-to-follow instructions, illustrations, and real-world examples that educators can readily understand, this is an essential resource for school leaders who want to create meaningful, data-driven decisions! *Note: The author created web site that provides information and activities paralleling the individual chapters of the second education of Schools and Data text is NO LONGER AVAILABLE.
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!