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| examples of data analysis in education: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 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. |
| examples of data analysis in education: Quantitative Data Analysis in Education Paul Connolly, 2007-08-07 An accessible and user-friendly guide to quantitative data analysis in educational research, aimed at those with little or no prior knowledge of statistical methods. |
| examples of data analysis in education: Data Analysis for Continuous School Improvement Victoria Bernhardt, 2013-10-18 5 PERCEPTIONS; Changing Perceptions; Assessing Perceptions; Questionnaires; Designing Questionnaires: Begin With the End in Mind; Data Collection Considerations; Our Example Schools; Study Questions for Perceptions; Summary; 6 STUDENT LEARNING; Ways to Measure Student Learning; Grades; Analyzing the Results, Descriptively; Analyzing the Results, Inferentially; Measurement Error; Looking Across Student Learning Measures; Other Common Testing Terms; Our Example Schools; Study Questions for Student Learning; Summary; 7 SCHOOL PROCESSES; School Level Processes; Classroom Level Processes. |
| examples of data analysis in education: You Are a Data Person Amelia Parnell, 2023-07-03 Internal and external pressure continues to mount for college professionals to provide evidence of successful activities, programs, and services, which means that, going forward, nearly every campus professional will need to approach their work with a data-informed perspective.But you find yourself thinking “I am not a data person”.Yes, you are. Or can be with the help of Amelia Parnell.You Are a Data Person provides context for the levels at which you are currently comfortable using data, helps you identify both the areas where you should strengthen your knowledge and where you can use this knowledge in your particular university role.For example, the rising cost to deliver high-quality programs and services to students has pushed many institutions to reallocate resources to find efficiencies. Also, more institutions are intentionally connecting classroom and cocurricular learning experiences which, in some instances, requires an increased gathering of evidence that students have acquired certain skills and competencies. In addition to programs, services, and pedagogy, professionals are constantly monitoring the rates at which students are entering, remaining enrolled in, and leaving the institution, as those movements impact the institution’s financial position.From teaching professors to student affairs personnel and beyond, Parnell offers tangible examples of how professionals can make data contributions at their current and future knowledge level, and will even inspire readers to take the initiative to engage in data projects.The book includes a set of self-assessment questions and a companion set of action steps and available resources to help readers accept their identity as a data person. It also includes an annotated list of at least 20 indicators that any higher education professional can examine without sophisticated data analyses. |
| examples of data analysis in education: FirstSchool Sharon Ritchie, Laura Gutmann, 2014 FirstSchool is a groundbreaking framework for teaching minority and low-income children. Changing the conversation from improving test scores to improving school experiences, the text features lessons learned from eight elementary schools whose leadership and staff implemented sustainable changes. The authors detail how to use education research and data to provide a rationale for change; how to promote professional learning that is genuinely collaborative and respectful; and how to employ developmentally appropriate teaching strategies that focus on the needs of minority and low-income children. |
| examples of data analysis in education: Street Data Shane Safir, Jamila Dugan, 2021-02-12 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. |
| examples of data analysis in education: Critical Theory and Qualitative Data Analysis in Education Rachelle Winkle-Wagner, Jamila Lee-Johnson, Ashley N. Gaskew, 2018-07-04 Critical Theory and Qualitative Data Analysis in Education offers a path-breaking explanation of how critical theories can be used within the analysis of qualitative data to inform research processes, such as data collection, analysis, and interpretation. This contributed volume offers examples of qualitative data analysis techniques and exemplars of empirical studies that employ critical theory concepts in data analysis. By creating a clear and accessible bridge between data analysis and critical social theories, this book helps scholars and researchers effectively translate their research designs and findings to multiple audiences for more equitable outcomes and disruption of historical and contemporary inequality. |
| examples of data analysis in education: Driven by Data Paul Bambrick-Santoyo, 2010-04-12 Offers a practical guide for improving schools dramatically that will enable all students from all backgrounds to achieve at high levels. Includes assessment forms, an index, and a DVD. |
| examples of data analysis in education: Data Analysis for Continuous School Improvement Victoria L. Bernhardt, 2017-09-19 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. |
| examples of data analysis in education: Using Data in Schools to Inform Leadership and Decision Making Alex J. Bowers, Alan R. Shoho, Bruce G. Barnett, 2014-11-01 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? |
| examples of data analysis in education: Leveraging Data for Student Success Laura G. Knapp, Elizabeth Glennie, Karen J. Charles, 2016-09-29 People providing services to schools, teachers, and students want to know whether these services are effective. With that knowledge, a project director can expand services that work well and adjust implementation of activities that are not working as expected. When finding that an innovative strategy benefits students, a project director might want to share that information with other service providers who could build upon that strategy. Some organizations that fund programs for students will want a report demonstrating the program’s success. Determining whether a program is effective requires expertise in data collection, study design, and analysis. Not all project directors have this expertise—they tend to be primarily focused on working with schools, teachers, and students to undertake program activities. Collecting and obtaining student-level data may not be a routine part of the program. This book provides an overview of the process for evaluating a program. It is not a detailed methodological text but focuses on awareness of the process. What do program directors need to know about data and data analysis to plan an evaluation or to communicate with an evaluator? Examples focus on supporting college and career readiness programs. Readers can apply these processes to other studies that include a data collection component. |
| examples of data analysis in education: A Framework for K-12 Science Education National Research Council, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on a Conceptual Framework for New K-12 Science Education Standards, 2012-02-28 Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments. |
| examples of data analysis in education: Using Data to Improve Learning Anthony Shaddock, 2014-02-01 Using Data to Improve Learning: A practical guide for busy teachers |
| examples of data analysis in education: Handbook of Data Analysis Melissa A Hardy, Alan Bryman, 2009-06-17 ′This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond′ - Environment and Planning ′The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher′ - Clive Seale, Brunel University ′With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts. ′ - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa ′This is an excellent guide to current issues in the analysis of social science data. I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments′ - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. The book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis. In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis. |
| examples of data analysis in education: The Datafication of Education Juliane Jarke, Andreas Breiter, 2020-05-21 This book attends to the transformation of processes and practices in education, relating to its increasing digitisation and datafication. The introduction of new means to measure, capture, describe and represent social life in numbers has not only transformed the ways in which teaching and learning are organised, but also the ways in which future generations (will) construct reality with and through data. Contributions consider data practices that span across different countries, educational fields and governance levels, ranging from early childhood education, to schools, universities, educational technology providers, to educational policy making and governance. The book demonstrates how digital data not only support decision making, but also fundamentally change the organisation of learning and teaching, and how these transformation processes can have partly ambivalent consequences, such as new possibilities for participation, but also the monitoring and emergence/manifestation of inequalities. Focusing on how data can drive decision making in education and learning, this book will be of interest to those studying both educational technology and educational policy making. The chapters in this book were originally published in Learning, Media and Technology. Chapter 4 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license. |
| examples of data analysis in education: Contemporary Technologies in Education Olusola O. Adesope, A.G. Rud, 2018-11-08 This edited volume provides a critical discussion of theoretical, methodological, and practical developments of contemporary forms of educational technologies. Specifically, the book discusses the use of contemporary technologies such as the Flipped Classroom (FC), Massive Open Online Course (MOOC), Social Media, Serious Educational Games (SEG), Wikis, innovative learning software tools, and learning analytic approach for making sense of big data. While some of these contemporary educational technologies have been touted as panaceas, researchers and developers have been faced with enormous challenges in enhancing the use of these technologies to arouse student attention and improve persistent motivation, engagement, and learning. Hence, the book examines how contemporary technologies can engender student motivation and result in improved engagement and learning. Each chapter also discusses the road ahead and where appropriate, uses the current trend to predict future affordances of technologies. |
| examples of data analysis in education: The Global Purpose Approach Tarece Johnson Ed.D., 2018-02-15 I created The Global Purpose Approach to help people gain the interest, knowledge, and skills needed for them to become more global-minded individuals. The idea for the book evolved from the need to enhance instruction in multicultural education so that people may be more knowledgeable, respectful, and appreciative of the geographies, languages, and cultures of people across the globe. The principles, content, and strategies that result comprise The Global Purpose Approachan instructional framework to introduce and integrate multiculturalism in schools, classrooms, private corporations, public agencies, and individuals daily lives. The purpose of The Global Purpose Approach is to foster multicultural awareness and education by helping to understand, respect, and appreciate similarities and differences across cultures. It consists of six guiding principlespurpose, environment, observation, perspective, language, and exploration (PEOPLE)together with an outline (NO FEAR) and resources for interactive and immersive learning of the core elements of culture. The intended impact of The Global Purpose Approach is to develop people who have enhanced knowledge and appreciation of diverse cultures and who can, therefore, interact and contribute innovatively in an interconnected, global society. Human resource professionals, organizational leaders, school districts, and teachers may use this book as a guide to integrate multicultural education in their departments, organizations, school systems, and classrooms. It also allows for educators to easily adapt the approach to fit their curriculum requirements and existing teaching plans and to implement it within their daily teaching. In this way, The Global Purpose Approach can be seen as a way to strengthen current teaching practices by adopting a set of philosophies and utilizing activities to foster more globally connected, engaged citizens without the need to fully revise or change. |
| examples of data analysis in education: Data-based Decision Making in Education Kim Schildkamp, Mei Kuin Lai, Lorna Earl, 2012-09-18 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. |
| examples of data analysis in education: A Framework for Transforming Learning in Schools Helen Timperley, Linda Kaser, Judy Halbert, 2014 |
| examples of data analysis in education: Big Data on Campus Karen L. Webber, Henry Y. Zheng, 2020-11-03 Webber, Henry Y. Zheng, Ying Zhou |
| examples of data analysis in education: Data Wise, Revised and Expanded Edition Kathryn Parker Boudett, Elizabeth A. City, Richard J. Murnane, 2020-08-26 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. |
| examples of data analysis in education: Statistical Analysis for Education and Psychology Researchers Ian Peers, 2006-02-27 Basic statistical concepts such as probability, estimation and inference, and their role in research design and analysis are presented in this volume. The author demonstrates which statistical test to use in given circumstances and how to use it, drawing on data from psychology and education.; Written for those without a strong mathematical background, the book's examples can be worked using a pocket calculator. Real life data are analyzed using statistical software (SAS), output is interpreted, and a decision chart is presented which summarizes considerations when choosing a statistical test. |
| examples of data analysis in education: Dear Data Giorgia Lupi, Stefanie Posavec, 2016-09-13 Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates the infinitesimal, incomplete, imperfect, yet exquisitely human details of life, in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere. |
| examples of data analysis in education: Introduction to Educational Research W. Newton Suter, 2012 W. Newton Suter argues that what is important in a changing education landscape is the ability to think clearly about research methods, reason through complex problems and evaluate published research. He explains how to evaluate data and establish its relevance. |
| examples of data analysis in education: Data Mining and Learning Analytics Samira ElAtia, Donald Ipperciel, Osmar R. Zaïane, 2016-09-20 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. |
| examples of data analysis in education: Big Data in Education Ben Williamson, 2017-07-24 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! |
| examples of data analysis in education: Interactive Graphics for Data Analysis Martin Theus, Simon Urbanek, 2008-10-24 Interactive Graphics for Data Analysis: Principles and Examples discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets.Fundamentals of Interactive Statistical GraphicsThe first part of the book summarizes principles and methodology, demons |
| examples of data analysis in education: The Basics of Data Literacy Michael Bowen, Anthony Bartley, 2014 Here's the ideal statistics book for teachers with no statistical background. Written in an informal style with easy-to-grasp examples, The Basics of Data Literacy teaches you how to help your students understand data. Then, in turn, they learn how to collect, summarize, and analyze statistics inside and outside the classroom. The books 10 succinct chapters provide an introduction to types of variables and data, ways to structure and interpret data tables, simple statistics, and survey basics from a student perspective. The appendices include hands-on activities tailored to middle and high school investigations. Because data are so central to many of the ideas in the Next Generation Science Standards, the ability to work with such information is an important science skill for both you and your students. This accessible book will help you get over feeling intimidated as your students learn to evaluate messy data on the Internet, in the news, and in future negotiations with car dealers and insurance agents. |
| examples of data analysis in education: Statistical Methods for Experimental Research in Education and Psychology Jimmie Leppink, 2019-05-30 This book focuses on experimental research in two disciplines that have a lot of common ground in terms of theory, experimental designs used, and methods for the analysis of experimental research data: education and psychology. Although the methods covered in this book are also frequently used in many other disciplines, including sociology and medicine, the examples in this book come from contemporary research topics in education and psychology. Various statistical packages, commercial and zero-cost Open Source ones, are used. The goal of this book is neither to cover all possible statistical methods out there nor to focus on a particular statistical software package. There are many excellent statistics textbooks on the market that present both basic and advanced concepts at an introductory level and/or provide a very detailed overview of options in a particular statistical software programme. This is not yet another book in that genre. Core theme of this book is a heuristic called the question-design-analysis bridge: there is a bridge connecting research questions and hypotheses, experimental design and sampling procedures, and common statistical methods in that context. Each statistical method is discussed in a concrete context of a set of research question with directed (one-sided) or undirected (two-sided) hypotheses and an experimental setup in line with these questions and hypotheses. Therefore, the titles of the chapters in this book do not include any names of statistical methods such as ‘analysis of variance’ or ‘analysis of covariance’. In a total of seventeen chapters, this book covers a wide range of topics of research questions that call for experimental designs and statistical methods, fairly basic or more advanced. |
| examples of data analysis in education: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| examples of data analysis in education: Schools and Data Theodore B. Creighton, 2006-07-06 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. |
| examples of data analysis in education: Research Basics James V. Spickard, 2016-09-15 Research Basics: Design to Data Analysis in Six Steps offers a fresh and creative approach to the research process based on author James V. Spickard’s decades of teaching experience. Using an intuitive six-step model, readers learn how to craft a research question and then identify a logical process for answering it. Conversational writing and multi-disciplinary examples illuminate the model’s simplicity and power, effectively connecting the “hows” and “whys” behind social science research. Students using this book will learn how to turn their research questions into results. |
| examples of data analysis in education: Letting Data Lead Eileen Depka, 2019 In Responding to Data, author Eileen Depka expounds on how implementing a proficient system of assessment yields actionable data. In order to better respond to student needs, educators need the know-how of conducting proper assessment design, data collection, and data interpretation. This book provides systems, processes, and specific examples that help teachers thoroughly understand how to preplan assessments and collect the data available to them. The tools and templates outline how teachers can determine if the collective data indicate a response is needed and how teachers can then turn gathered information into immediate actions. By reading this book, educators gain strategies that build a culture of data analysis and student achievement in their classrooms-- |
| examples of data analysis in education: Research Methods in Education Joseph Check, Russell K. Schutt, 2011-10-27 Research Methods in Education introduces research methods as an integrated set of techniques for investigating questions about the educational world. This lively, innovative text helps students connect technique and substance, appreciate the value of both qualitative and quantitative methodologies, and make ethical research decisions. It weaves actual research stories into the presentation of research topics, and it emphasizes validity, authenticity, and practical significance as overarching research goals. The text is divided into three sections: Foundations of Research (5 chapters), Research Design and Data Collection (7 chapters), and Analyzing and Reporting Data (3 chapters). This tripartite conceptual framework honors traditional quantitative approaches while reflecting the growing popularity of qualitative studies, mixed method designs, and school-based techniques. This approach provides a comprehensive, conceptually unified, and well-written introduction to the exciting but complex field of educational research. |
| examples of data analysis in education: Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report Christine A. Franklin, 2007 Statistics education as proposed in this framework can promote the must-have competencies for graduates to thrive in the modern world. |
| examples of data analysis in education: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| examples of data analysis in education: Driven by Data 2.0 Paul Bambrick-Santoyo, 2019-04-16 The bestselling guide for school leaders—updated in a new edition Data-driven instruction is the philosophy that schools should focus on two simple questions: how do you know if are students learning? And when they are not, what do you do about it? Driven by Data 2.0 is a practical guide that answers these questions to empower schools to achieve significant gains in student achievement. Rooted in a proven framework that has been implemented in thousands of schools, the book presents what makes schools successful along with tools to put the framework into place to make data work for your schools: Assess—set the roadmap for learning Analyze—identify why students struggle Act—teach more effectively what students need Build the culture—train and develop your staff so that data-driven instruction can thrive If you’re a K – 12 leader, coach, or teacher looking to implement data-driven instruction in your school district, Driven by Data 2.0 has the tools to train your staff: PD materials, videos of exemplar practice and all the resources you need to achieve remarkable results. |
| examples of data analysis in education: Design and Analysis in Educational Research Kamden K. Strunk, Mwarumba Mwavita, 2020-04-02 NEW: updated eResources, 'Case Studies for Teaching on Race, Racism and Black Lives Matter.' Please see Support Material tab to download the new resources. This book presents an integrated approach to learning about research design alongside statistical analysis concepts. Strunk and Mwavita maintain a focus on applied educational research throughout the text, with practical tips and advice on how to do high-quality quantitative research. Design and Analysis in Educational Research teaches research design (including epistemology, research ethics, forming research questions, quantitative design, sampling methodologies, and design assumptions) and introductory statistical concepts (including descriptive statistics, probability theory, sampling distributions), basic statistical tests (like z and t), and ANOVA designs, including more advanced designs like the factorial ANOVA and mixed ANOVA, using SPSS for analysis. Designed specifically for an introductory graduate course in research design and statistical analysis, the book takes students through principles by presenting case studies, describing the research design principles at play in each study, and then asking students to walk through the process of analyzing data that reproduce the published results. An online eResource is also available with data sets. This textbook is tailor-made for first-level doctoral courses in research design and analysis, and will also be of interest to graduate students in education and educational research. |
| examples of data analysis in education: Data Analysis for Social Science Elena Llaudet, Kosuke Imai, 2022-11-29 Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors-- |
| examples of data analysis in education: Essential First Steps to Data Analysis Carol S. Parke, 2012-12-13 Carol S. Parke's Essential First Steps to Data Analysis: Scenario-Based Examples Using SPSS provides instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Such preparation may involve data management and manipulation tasks, data organization, structural changes to the data files, or conducting preliminary analysis. Twelve research-based scenarios are used to present the content. Each scenario tells the story of a researcher who thoroughly examined their data and the decisions they made along the way. The scenario begins with a description of the researcher's study and his/her data file(s), then describes the issues the researcher must address, explains why they are important, shows how SPSS was used to address the issues and prepare data, and shares the researcher's reflections and any additional decision-making. Finally, each scenario ends with the researcher's written summary of the procedures and outcomes from the initial data preparation or analysis. |
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