Quantitative Data Analysis in Education


Book Description

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.




Big Data in Education


Book Description

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!




Data Science in Education Using R


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.




Education Data Done Right


Book Description

Lots of pundits and researchers have ideas about how education data work should be done and who should do it. Many of their ideas are disseminated widely. This book covers the things those books do not: metadata, working with enterprise IT, data requests, politics, and why descriptive statistics can take you far.




Essentials of Geophysical Data Processing


Book Description

A concise introduction to geophysical data processing - many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The textbook begins with calculus before transitioning to discrete time series via the sampling theorem, aliasing, use of complex sinusoids, development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, or statistics, this textbook draws scenarios and datasets from across the world of geophysics, and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises (using MATLAB or similar computing environment). Online supplementary resources include datasets for students, and a solutions manual and all the figures from the book as PowerPoints for course instructors.




Data Wise, Revised and Expanded Edition


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.




Driven by Data


Book Description

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.




Knowledge Graphs and Big Data Processing


Book Description

This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.




Data Analytics Applications in Education


Book Description

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.




Leveraging Data for Student Success


Book Description

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.