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The chief executive officer of a corporation is not much different from a public school administrator. While CEOs base many of their decisions on data, for school administrators, this type of research may conjure up miserable memories of searching for information to meet a graduate school requirement. However, the value of data-based decision making will continue to escalate and the school community—students, teachers, parents and the general public—expect this information to come from their administrators. Administrators are called on to be accountable, but few are capable of presenting the mountain of data that they collect in a cohesive and strategic manner. Most statistical books are focused on statistical theory versus application, but Statistics Made Simple for School Leaders presents statistics in a simple, practical, conceptual, and immediately applicable manner. It enables administrators to take their data and manage it into strategic information so the results can be used for action plans that benefit the school system. The approach is 'user friendly' and leaves the reader with a confident can-do attitude to communicate results and plans to staff and the community.
Our fifth book in the International Research on School Leadership series focuses on the use of data in schools and districts as useful information for leadership and decision making. Schools are awash in data and information, from test scores, to grades, to discipline reports, and attendance as just a short list of student information sources, while additional streams of data feed into schools and districts from teachers and parents as well as local, regional and national policy levels. To deal with the data, schools have implemented a variety of data practices, from data rooms, to data days, data walks, and data protocols. However, despite the flood of data, successful school leaders are leveraging an analysis of their school’s data as a means to bring about continuous improvement in an effort to improve instruction for all students. Nevertheless, some drown, some swim, while others find success. Our goal in this book volume is to bring together a set of chapters by authors who examine successful data use as it relates to leadership and school improvement. In particular, the chapters in this volume consider important issues in this domain, including: • How educational leaders use data to inform their practice. • What types of data and data analysis are most useful to successful school leaders. • To what extent are data driven and data informed practices helping school leaders positively change instructional practice? • In what ways does good data collection and analysis feed into successful continuous improvement and holistic systems thinking? • How have school leadership practices changed as more data and data analysis techniques have become available? • What are the major obstacles facing school leaders when using data for decision making and how do they overcome them?
The monitoring of data within educational institutions is essential to ensure the success of its students and faculty. By continually analyzing data, educational leaders can increase quality and productivity in their institutions. Data Leadership for K-12 Schools in a Time of Accountability explores techniques and processes of educational data analysis and its application in developing solutions and systems for instructional concerns and next-generation learning. Providing extensive research covering areas such as data-driven culture, student accountability, and data dissemination, this unique reference is essential for principals, administrators, practitioners, academicians, students, and educational consultants looking to maximize their institution’s performance.
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.
Educational Data Analytics (EDA) have been attributed with significant benefits for enhancing on-demand personalized educational support of individual learners as well as reflective course (re)design for achieving more authentic teaching, learning and assessment experiences integrated into real work-oriented tasks. This open access textbook is a tutorial for developing, practicing and self-assessing core competences on educational data analytics for digital teaching and learning. It combines theoretical knowledge on core issues related to collecting, analyzing, interpreting and using educational data, including ethics and privacy concerns. The textbook provides questions and teaching materials/ learning activities as quiz tests of multiple types of questions, added after each section, related to the topic studied or the video(s) referenced. These activities reproduce real-life contexts by using a suitable use case scenario (storytelling), encouraging learners to link theory with practice; self-assessed assignments enabling learners to apply their attained knowledge and acquired competences on EDL. By studying this book, you will know where to locate useful educational data in different sources and understand their limitations; know the basics for managing educational data to make them useful; understand relevant methods; and be able to use relevant tools; know the basics for organising, analysing, interpreting and presenting learner-generated data within their learning context, understand relevant learning analytics methods and be able to use relevant learning analytics tools; know the basics for analysing and interpreting educational data to facilitate educational decision making, including course and curricula design, understand relevant teaching analytics methods and be able to use relevant teaching analytics tools; understand issues related with educational data ethics and privacy. This book is intended for school leaders and teachers engaged in blended (using the flipped classroom model) and online (during COVID-19 crisis and beyond) teaching and learning; e-learning professionals (such as, instructional designers and e-tutors) of online and blended courses; instructional technologists; researchers as well as undergraduate and postgraduate university students studying education, educational technology and relevant fields.
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.
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.
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.
The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student’ s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.
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!