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Data! Dialogue! Decisions!: The Data Difference
by Brian M. Pete Catherine A. DuncanLink relevant data to results instantly and consistently!This powerful text offers school leaders a process for data-based decision making that includes the critical elements of school improvement: collaborative teams, meaningful data, and measurable results. Administrators and instructors select the data, dialogue about the findings, and then make informed decisions about improving student performance. Educators will learn to:Select data that is easily accessible, collectible on an ongoing basis, and capable of impacting student achievementUse the three-step cyclical model of data analysisCreate and assess goals that are specific, measurable, and results-oriented
Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings (Lecture Notes in Computer Science #10474)
by Katrien Verbert Hendrik Drachsler Élise Lavoué Mar Pérez-Sanagustín Julien BroisinThis book constitutes the proceedings of the 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, held in Tallinn, Estonia, in September 2017. The 24 full papers, 23 short papers, 6 demo papers, and 22 poster papers presented in this volume were carefully reviewed and selected from 141 submissions. The theme for the 12th EC-TEL conference on Data Driven Approaches in Digital Education' aims to explore the multidisciplinary approaches that eectively illustrate how data-driven education combined with digital education systems can look like and what are the empirical evidences for the use of datadriven tools in educational practices.
Data-Driven Decision Making and Dynamic Planning: A School Leader's Guide
by Paul PreussThis book will help you understand how to integrate data-based decisions into the daily work of the school. It is a practical and relevant handbook for converting data into wise decision-making and planning. It will give you the skills to successfully make data-based decisions, measure student learning and program effectiveness, evaluate student progress, use data to improve instruction, integrate a "Dynamic Planning" process into the daily operation of your school.
Data-Driven Decision-Making in Schools: Lessons from Trinidad
by Jennifer Yamin-AliYamin-Ali shows how schools can undertake responsible decision-making through gathering and evaluating data, using as examples six fully developed case studies that shed light on common questions of school culture and student life, including student stress, subject selection, and the role of single-sex classes.
Data-Driven Design for Computer-Supported Collaborative Learning: Design Matters (Lecture Notes in Educational Technology)
by Lanqin ZhengThis book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design collaborative learning activities based on data-driven design approaches, while the second shares illustrative examples of computer-supported collaborative learning activities. In turn, the third and last section demonstrates how to evaluate design quality and the fidelity of enactment based on design-centered research.The book features several examples of innovative data-driven design approaches to optimizing collaborative learning activities; highlights innovative CSCL activities in authentic learning environments; demonstrates how learning analytics can be used to optimize CSCL design; and discusses the design-centered research approach to evaluating the alignment between design and enactment in CSCL. Given its scope, it will be of interest to a broad readership including researchers, educators, practitioners, and students in the field of collaborative learning, as well as the rapidly growing community of people who are interested in optimizing learning performance with CSCL.
Data Driven Differentiation in the Standards-Based Classroom
by Gayle H. Gregory Linda M. KuzmichCollect the data you need to reach every student! Veteran educators Gregory and Kuzmich provide user-friendly techniques for data-gathering, helping you to differentiate instruction. This informative book is now fully updated to support the Common Core and other key standards, and includes: Step-by-step guidance on gathering data to improve classroom dynamics, pinpoint student learning styles, adjust lessons for different learners, and inform diagnostic teaching and assessment Techniques for using data to refresh and strengthen curriculum, including numerous unit and lesson plans fully linked with the Common Core A wealth of templates for fast and simple data collection Updated differentiation strategies for the Common Core and other key standards, including the Career and College Readiness Standards and the Standards of Mathematical Practice
Data Driven Differentiation in the Standards-Based Classroom
by Gayle H. Gregory Linda M. KuzmichCollect the data you need to reach every student! Veteran educators Gregory and Kuzmich provide user-friendly techniques for data-gathering, helping you to differentiate instruction. This informative book is now fully updated to support the Common Core and other key standards, and includes: Step-by-step guidance on gathering data to improve classroom dynamics, pinpoint student learning styles, adjust lessons for different learners, and inform diagnostic teaching and assessment Techniques for using data to refresh and strengthen curriculum, including numerous unit and lesson plans fully linked with the Common Core A wealth of templates for fast and simple data collection Updated differentiation strategies for the Common Core and other key standards, including the Career and College Readiness Standards and the Standards of Mathematical Practice
Data-Driven Instructional Leadership
by Rebecca J. BlinkWith real-world examples from actual schools, this book shows you how to nurture a culture of continuous improvement, meet the needs of individual students, foster an environment of high expectations, and meet the requirements of NCLB.
Data-Driven Leadership
by Amanda Datnow Vicki ParkTools and techniques from the trailblazers in data-based education reformOver 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
Data-Driven Learning for the Next Generation: Corpora and DDL for Pre-tertiary Learners
by Peter CrosthwaiteDespite advancements in and availability of corpus software in language classrooms facilitating data-driven learning (DDL), the use of such methods with pre-tertiary learners remains rare. This book specifically explores the affordances of DDL for younger learners, testing its viability with teachers and students at the primary and secondary years of schooling. It features eminent and up-and-coming researchers from Europe, Asia, and Australasia who seek to address best practice in implementing DDL with younger learners, while providing a wealth of empirical findings and practical DDL activities ready for use in the pre-tertiary classroom. Divided into three parts, the volume's first section focuses on overcoming emerging challenges for DDL with younger learners, including where and how DDL can be integrated into pre-tertiary curricula, as well as potential barriers to this integration. It then considers new, cutting-edge innovations in corpora and corpus software for use with younger learners in the second section, before reporting on actual DDL studies performed with younger learners (and/or their teachers) at the primary and secondary levels of education. This book will appeal to post-graduate students, academics and researchers with interests in corpus linguistics, second language acquisition, primary and secondary literacy education, and language and educational technologies.
The Data-Driven School: Collaborating to Improve Student Outcomes (The Guilford Practical Intervention in the Schools Series)
by Daniel M. Hyson Joseph F. Kovaleski Benjamin Silberglitt PhD Jason A. PedersenThis indispensable practitioner's guide helps to build the capacity of school psychologists, administrators, and teachers to use data in collaborative decision making. It presents an applied, step-by-step approach for creating and running effective data teams within a problem-solving framework. The authors describe innovative ways to improve academic and behavioral outcomes at the individual, class, grade, school, and district levels. Applications of readily available technology tools are highlighted. In a large-size format with lay-flat binding for easy photocopying, the book includes learning activities and helpful reproducible forms. Purchasers can download and print the reproducible forms, as well as access Excel spreadsheets and PowerPoint slides related to the book, at the companion website. This book is in The Guilford Practical Intervention in the Schools Series, edited by Sandra M. Chafouleas.
Data Elicitation for Second and Foreign Language Research (Second Language Acquisition Research Ser.)
by Susan M. Gass Alison MackeyThis timely reference guide is specifically directed toward the needs of second language researchers, who can expect to gain a clearer understanding of which techniques may be most appropriate and fruitful in given research domains. Data Elicitation for Second and Foreign Language Research is a perfect companion to the same author team‘s bestsellin
Data-Enhanced Leadership
by Alan M. Blankstein Paul D. Houston Robert W. ColeThis compact volume combines research, practice, and innovative thinking to illustrate how strategic application of data enhances leadership practices and significantly improves curriculum, instruction, and schoolwide performance.
Data Entry Clerk: Passbooks Study Guide (Career Examination Series #C-3506)
by National Learning CorporationThe Data Entry Clerk Passbook® prepares you for your test by allowing you to take practice exams in the subjects you need to study. It provides hundreds of questions and answers in the areas that will likely be covered on your upcoming exam, including but not limited to: filing; name and number checking; and more.
Data Envelopment Analysis: A Handbook of Models and Methods (International Series in Operations Research & Management Science #221)
by Joe ZhuThis handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U. S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball. Chapter 6 examines efficiency and productivity of U. S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education. Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U. S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.
Data for Continuous Programmatic Improvement: Steps Colleges of Education Must Take to Become a Data Culture (Routledge Research in Higher Education)
by Ellen B. Mandinach Edith GummerThis book addresses the issue of data use in educator preparation programs towards continuous programmatic improvement. With an aim to increase the rigor in both research and practice in educational administration and teacher education, this volume will analyze the longstanding quality concerns about teacher and leadership preparation and standards for programs and educators, as well as controversies concerning national accreditation and federal efforts to mandate program reporting data. By exploring the policies and practices that influence departments of education, this volume examines the increasing pressures to improve institutional functioning, within a complex system of university, state, and national structures and organizations.
The Data Guidebook for Teachers and Leaders: Tools for Continuous Improvement
by Eileen DepkaAre you looking for new ways to use data in the decision-making process? Are you seeking tools that provide better flow-through from data to improved student achievement? Have you ever considered including students in the data-to-improvement cycle? Schools recognize that data is an essential decision-making tool, but it requires teamwork and reflection to reap the maximum benefits. This guidebook offers practical collection and analysis methods and templates as well as tips for building trust and working together.
The Data Guidebook for Teachers and Leaders: Tools for Continuous Improvement
by Eileen M. DepkaThis book offers practical methods, templates, and rubrics for collecting and analyzing data, and includes innovative ideas for building trust, including students in the process, and working together.
Data Management and Analysis: Case Studies in Education, Healthcare and Beyond (Studies in Big Data #65)
by Reda Alhajj Mohammad Moshirpour Behrouz FarData management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
Data Management Technologies and Applications: 10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022, Revised Selected Papers (Communications in Computer and Information Science #1860)
by Alfredo Cuzzocrea Oleg Gusikhin Slimane Hammoudi Christoph QuixThis book constitutes the refereed post-proceedings of the 10th International Conference and 11th International Conference on Data Management Technologies and Applications, DATA 2021 and DATA 2022, was held virtually due to the COVID-19 crisis on July 6–8, 2021 and in Lisbon, Portugal on July 11-13, 2022.The 11 full papers included in this book were carefully reviewed and selected from 148 submissions. They were organized in topical sections as follows: engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects of information systems and technology involving advanced applications of data.
Data Management Technologies and Applications: 12th International Conference, DATA 2023, Rome, Italy, July 11–13, 2023, Revised Selected Papers (Communications in Computer and Information Science #2105)
by Alfredo Cuzzocrea Slimane Hammoudi Oleg GusikhinThis book constitutes the proceedings of the 12th International Conference on Data Management Technologies and Applications, DATA 2023 , held in Rome,Italy during July 11–13, 2023, Proceedings. The 6 full paper were carefully reviewed and selected from 106 submissions. The papers are organized in subject areas as follows: Big Data Applications, Data Analytics, Data Science, NoSQL Databases, Social Data Analytics, Dimensional Modelling, Deep Learning and Big Data, Decision Support Systems, Data Warehouse Management and Data Management for Analytics.
Data Management Technologies and Applications: 9th International Conference, DATA 2020, Virtual Event, July 7–9, 2020, Revised Selected Papers (Communications in Computer and Information Science #1446)
by Slimane Hammoudi Christoph Quix Jorge BernardinoThis book constitutes the thoroughly refereed proceedings of the 9th International Conference on Data Management Technologies and Applications, DATA 2020, which was supposed to take place in Paris, France, in July 2020. Due to the Covid-19 pandemic the event was held virtually. The 14 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: datamining; decision support systems; data analytics; data and information quality; digital rights management; big data; knowledge management; ontology engineering; digital libraries; mobile databases; object-oriented database systems; data integrity.
Data Mining: 17th Australasian Conference, AusDM 2019, Adelaide, SA, Australia, December 2–5, 2019, Proceedings (Communications in Computer and Information Science #1127)
by Thuc D. Le Kok-Leong Ong Yanchang Zhao Warren H. Jin Sebastien Wong Lin Liu Graham WilliamsThis book constitutes the refereed proceedings of the 17th Australasian Conference on Data Mining, AusDM 2019, held in Adelaide, SA, Australia, in December 2019.The 20 revised full papers presented were carefully reviewed and selected from 56 submissions. The papers are organized in sections on research track, application track, and industry showcase.
Data Mining: 20th Australasian Conference, AusDM 2022, Western Sydney, Australia, December 12–15, 2022, Proceedings (Communications in Computer and Information Science #1741)
by Laurence A. F. Park Heitor Murilo Gomes Maryam Doborjeh Yee Ling Boo Yun Sing Koh Yanchang Zhao Graham Williams Simeon SimoffThis book constitutes the refereed proceedings of the 20th Australasian Conference on Data Mining, AusDM 2022, held in Western Sydney, Australia, during December 12–15, 2022. The 17 full papers included in this book were carefully reviewed and selected from 44 submissions. They were organized in topical sections as research track and application track.
Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17–22, 2018, Proceedings (Lecture Notes in Computer Science #10943)
by Ying Tan Yuhui Shi Qirong TangThis book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018. The 74 papers presented in this volume were carefully reviewed and selected from 126 submissions. They are organized in topical sections named: database, data preprocessing, matrix factorization, data analysis, visualization, visibility analysis, clustering, prediction, classification, pattern discovery, text mining and knowledge management, recommendation system in social media, deep learning, big data, Industry 4.0, practical applications