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Author: Colby Tofel-Grehl
Publisher: Taylor & Francis
ISBN: 1040030157
Category : Education
Languages : en
Pages : 208
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Book Description
Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, “how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?” whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms.
Author: Colby Tofel-Grehl
Publisher: Taylor & Francis
ISBN: 1040030157
Category : Education
Languages : en
Pages : 208
Get Book Here
Book Description
Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, “how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?” whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms.
Author: Shane Safir
Publisher: Corwin
ISBN: 1071812661
Category : Education
Languages : en
Pages : 281
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Book Description
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.
Author: Ryan A. Estrellado
Publisher: Routledge
ISBN: 1000200906
Category : Education
Languages : en
Pages : 331
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Book Description
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.
Author: John Fantuzzo
Publisher: Springer
ISBN: 1137475110
Category : Political Science
Languages : en
Pages : 196
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Book Description
Multifaceted social problems like disaster relief, homelessness, health care, and academic achievement gaps cannot be adequately addressed with isolated and disconnected public service agencies. The Actionable Intelligence for Social Policy model addresses the limitations to traditional approaches to American public administration.
Author: Jane Margolis
Publisher: MIT Press
ISBN: 0262533464
Category : Education
Languages : en
Pages : 245
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Book Description
Why so few African American and Latino/a students study computer science: updated edition of a book that reveals the dynamics of inequality in American schools. The number of African Americans and Latino/as receiving undergraduate and advanced degrees in computer science is disproportionately low. And relatively few African American and Latino/a high school students receive the kind of institutional encouragement, educational opportunities, and preparation needed for them to choose computer science as a field of study and profession. In Stuck in the Shallow End, Jane Margolis and coauthors look at the daily experiences of students and teachers in three Los Angeles public high schools: an overcrowded urban high school, a math and science magnet school, and a well-funded school in an affluent neighborhood. They find an insidious “virtual segregation” that maintains inequality. The race gap in computer science, Margolis discovers, is one example of the way students of color are denied a wide range of occupational and educational futures. Stuck in the Shallow End is a story of how inequality is reproduced in America—and how students and teachers, given the necessary tools, can change the system. Since the 2008 publication of Stuck in the Shallow End, the book has found an eager audience among teachers, school administrators, and academics. This updated edition offers a new preface detailing the progress in making computer science accessible to all, a new postscript, and discussion questions (coauthored by Jane Margolis and Joanna Goode).
Author: Amanda L. Golbeck
Publisher: Springer Nature
ISBN: 3030600602
Category : Business & Economics
Languages : en
Pages : 431
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Book Description
This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture.
Author: Amanda Datnow
Publisher: John Wiley & Sons
ISBN: 1118234081
Category : Education
Languages : en
Pages : 111
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Book Description
Tools and techniques from the trailblazers in data-based education reform Over a period of several years, Amanda Datnow and Vicki Park visited public schools with a reputation for being ahead of the pack in data-driven decision making. The results of this pioneering study reveal how education leaders can make data work for students and teachers, rather than against them. This book is an essential guide to meeting the challenges of high-stakes accountability, building performance-based schools, and improving student outcomes. By following the advice in this book, you’ll be able to transform data overload into a data-positive school culture. You’ll learn the difference between “data-driven leadership” and “data-informed leadership,” and how to use distributed leadership to inspire collaboration and guided analysis. Incorporating narrative reflections drawn from real educators and administrators, the authors refine their observations and interviews into practical conclusions that leaders can put to use immediately. This book empowers leaders to support inquiry, build trust in data-based initiatives, establish goals for evidence use, and provide educators with the skills they need to mobilize data for the good of all stakeholders. “Datnow and Park’s ideas are easily accessible and grounded in clear examples, and their seven ‘calls’ about what needs to be done nail the problem and the solutions. Use this book as your action guide and you’ll be rewarded with better results in student learning.” —Michael Fullan, professor emeritus, University of Toronto “Datnow and Park uncover, at last, what it means to use data to inform leadership. Documenting the four P’s (people, policies, practices, and patterns) in schools, we learn about the organization and dynamics of reform informed by data. A must read!” —Ann Lieberman, senior scholar, Stanford University
Author: Simon Elias Bibri
Publisher: Springer
ISBN: 3030173127
Category : Political Science
Languages : en
Pages : 354
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Book Description
We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability.
Author: Kathryn Parker Boudett
Publisher: Harvard Education Press
ISBN: 1612505236
Category : Education
Languages : en
Pages : 459
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Book Description
Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning presents a clear and carefully tested blueprint for school leaders. It shows how examining test scores and other classroom data can become a catalyst for important schoolwide conversations that will enhance schools’ abilities to capture teachers’ knowledge, foster collaboration, identify obstacles to change, and enhance school culture and climate. This revised and expanded edition captures the learning that has emerged in integrating the Data Wise process into school practice and brings the book up-to-date with recent developments in education and technology including: The shift to the Common Core State Standards. New material on the “ACE Habits of Mind”: practices that prioritize Action, Collaboration, and Evidence as part of transforming school culture. A new chapter on “How We Improve,” based on experiences implementing Data Wise and to address two common questions: “Where do I start?” and “How long will it take?” Other revisions take into account changes in the roles of school data teams and instructional leadership teams in guiding the inquiry process. The authors have also updated exhibits, examples, and terminology throughout and have added new protocols and resources.
Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 218
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Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
