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| data analytics in education pdf: Adoption of Data Analytics in Higher Education Learning and Teaching Dirk Ifenthaler, David Gibson, 2020-08-10 The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education. |
| data analytics in education pdf: Big Data and Learning Analytics in Higher Education Ben Kei Daniel, 2016-08-27 This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns. |
| data analytics in education pdf: Big Data on Campus Karen L. Webber, Henry Y. Zheng, 2020-11-03 Webber, Henry Y. Zheng, Ying Zhou |
| data analytics in education pdf: 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. |
| data analytics in education pdf: Big Data in Education: Pedagogy and Research Theodosia Prodromou, 2021-10-04 This book discusses how Big Data could be implemented in educational settings and research, using empirical data and suggesting both best practices and areas in which to invest future research and development. It also explores: 1) the use of learning analytics to improve learning and teaching; 2) the opportunities and challenges of learning analytics in education. As Big Data becomes a common part of the fabric of our world, education and research are challenged to use this data to improve educational and research systems, and also are tasked with teaching coming generations to deal with Big Data both effectively and ethically. The Big Data era is changing the data landscape for statistical analysis, the ways in which data is captured and presented, and the necessary level of statistical literacy to analyse and interpret data for future decision making. The advent of Big Data accentuates the need to enable citizens to develop statistical skills, thinking and reasoning needed for representing, integrating and exploring complex information. This book offers guidance to researchers who are seeking suitable topics to explore. It presents research into the skills needed by data practitioners (data analysts, data managers, statisticians, and data consumers, academics), and provides insights into the statistical skills, thinking and reasoning needed by educators and researchers in the future to work with Big Data. This book serves as a concise reference for policymakers, who must make critical decisions regarding funding and applications. |
| data analytics in education pdf: 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! |
| data analytics in education pdf: 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. |
| data analytics in education pdf: 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. |
| data analytics in education pdf: 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. |
| data analytics in education pdf: Learning Analytics Johann Ari Larusson, Brandon White, 2014-07-04 In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics. Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world. Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to: Enhance student and faculty performance. Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. Encourage more efficient use of resources at the institutional level. Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success. |
| data analytics in education pdf: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| data analytics in education pdf: 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. |
| data analytics in education pdf: 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. |
| data analytics in education pdf: International Perspectives on School Settings, Education Policy and Digital Strategies Annika Wilmers, Sieglinde Jornitz, 2021-02-15 An exchange on education ideas has shaped the transatlantic discourse in education for a long time. Over the past two decades education science has increasingly become networked internationally. Since 2015, the Office for International Cooperation in Education at DIPF | Leibniz Institute for Research and Information in Education has organized international sessions on education research at the Annual Meetings of the American Educational Research Association, thus providing a floor for transatlantic exchange on current research topics. The volume gives an overview of the transatlantic activities in education research with regard to these sessions representing a collection of topics ranging from school development over the use of large scale assessment and digital data in education to questions related to migration and public education or the economization of education. At the same time the volume offers a reflection on the assets and obstacles of international exchange. |
| data analytics in education pdf: 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. |
| data analytics in education pdf: Radical Solutions and Learning Analytics Daniel Burgos, 2020-05-08 Learning Analytics become the key for Personalised Learning and Teaching thanks to the storage, categorisation and smart retrieval of Big Data. Thousands of user data can be tracked online via Learning Management Systems, instant messaging channels, social networks and other ways of communication. Always with the explicit authorisation from the end user, being a student, a teacher, a manager or a persona in a different role, an instructional designer can design a way to produce a practical dashboard that helps him improve that very user’s performance, interaction, motivation or just grading. This book provides a thorough approach on how education, as such, from teaching to learning through management, is improved by a smart analysis of available data, making visible and useful behaviours, predictions and patterns that are hinder to the regular eye without the process of massive data. |
| data analytics in education pdf: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data analytics in education pdf: Fundamentals of Machine Learning for Predictive Data Analytics, second edition John D. Kelleher, Brian Mac Namee, Aoife D'Arcy, 2020-10-20 The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. |
| data analytics in education pdf: Learning Analytics in Higher Education Jaime Lester, Carrie Klein, Huzefa Rangwala, Aditya Johri, 2017-12-21 Learning analytics (or educational big data) tools are increasingly being deployed on campuses to improve student performance, retention and completion, especially when those metrics are tied to funding. Providing personalized, real-time, actionable feedback through mining and analysis of large data sets, learning analytics can illuminate trends and predict future outcomes. While promising, there is limited and mixed empirical evidence related to its efficacy to improve student retention and completion. Further, learning analytics tools are used by a variety of people on campus, and as such, its use in practice may not align with institutional intent. This monograph delves into the research, literature, and issues associated with learning analytics implementation, adoption, and use by individuals within higher education institutions. With it, readers will gain a greater understanding of the potential and challenges related to implementing, adopting, and integrating these systems on their campuses and within their classrooms and advising sessions. This is the fifth issue of the 43rd volume of the Jossey-Bass series ASHE Higher Education Report. Each monograph is the definitive analysis of a tough higher education issue, based on thorough research of pertinent literature and institutional experiences. Topics are identified by a national survey. Noted practitioners and scholars are then commissioned to write the reports, with experts providing critical reviews of each manuscript before publication. |
| data analytics in education pdf: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data analytics in education pdf: Utilizing Learning Analytics to Support Study Success Dirk Ifenthaler, Dana-Kristin Mah, Jane Yin-Kim Yau, 2019-01-17 Students often enter higher education academically unprepared and with unrealistic perceptions and expectations of university life, which are critical factors that influence students’ decisions to leave their institutions prior to degree completion. Advances in educational technology and the current availability of vast amounts of educational data make it possible to represent how students interact with higher education resources, as well as provide insights into students’ learning behavior and processes. This volume offers new research in such learning analytics and demonstrates how they support students at institutions of higher education by offering personalized and adaptive support of their learning journey. It focuses on four major areas of discussion: · Theoretical perspectives linking learning analytics and study success. · Technological innovations for supporting student learning. · Issues and challenges for implementing learning analytics at higher education institutions. · Case studies showcasing successfully implemented learning analytics strategies at higher education institutions. Utilizing Learning Analytics to Support Study Success ably exemplifies how educational data and innovative digital technologies contribute to successful learning and teaching scenarios and provides critical insight to researchers, graduate students, teachers, and administrators in the general areas of education, educational psychology, academic and organizational development, and instructional technology. |
| data analytics in education pdf: Applications of Big Data Analytics Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya, 2019-02-09 This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA. |
| data analytics in education pdf: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| data analytics in education pdf: Visualizations and Dashboards for Learning Analytics Muhittin Sahin, Dirk Ifenthaler, 2021-12-16 This edited volume fills the gaps in existing literature on visualization and dashboard design for learning analytics. To do so, it presents critical tips to stakeholders and acts as guide to efficient implementation. The book covers the following topics: visualization and dashboard design for learning analytics, visualization and dashboard preferences of stakeholders, learners’ patterns on the dashboard, usability of visualization techniques and the dashboard, dashboard and intervention design, learning and instructional design for learning analytics, privacy and security issues about the dashboard, and future directions of visualization and dashboard design. This book will be of interest to researchers with interest in learning analytics and data analytics, teachers and students in higher education institutions and instructional designers, as it includes contributions from a wide variety of educational and psychological researchers, engineers, instructional designers, learning scientists, and computer scientists interested in learning analytics. |
| data analytics in education pdf: A General Introduction to Data Analytics João Moreira, Andre Carvalho, Tomás Horvath, 2018-07-18 A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms. |
| data analytics in education pdf: 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. |
| data analytics in education pdf: Deep Learning Techniques and Optimization Strategies in Big Data Analytics Thomas, J. Joshua, Karagoz, Pinar, Ahamed, B. Bazeer, Vasant, Pandian, 2019-11-29 Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry. |
| data analytics in education pdf: Big Data Analytics Beyond Hadoop Vijay Srinivas Agneeswaran, 2014-05-15 Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students. |
| data analytics in education pdf: 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. |
| data analytics in education pdf: 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. |
| data analytics in education pdf: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analytics in education pdf: Radical Solutions and Open Science Daniel Burgos, 2020-05-14 This open access book presents how Open Science is a powerful tool to boost Higher Education. The book introduces the reader into Open Access, Open Technology, Open Data, Open Research results, Open Licensing, Open Accreditation, Open Certification, Open Policy and, of course, Open Educational Resources. It brings all these key topics from major players in the field; experts that present the current state of the art and the forthcoming steps towards a useful and effective implementation. This book presents radical, transgenic solutions for recurrent and long-standing problems in Higher Education. Every chapter presents a clear view and a related solution to make Higher Education progress and implement tools and strategies to improve the user’s performance and learning experience. This book is part of a trilogy with companion volumes on Radical Solutions & Learning Analytics and Radical Solutions & eLearning. |
| data analytics in education pdf: Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning Rani, Geeta, Tiwari, Pradeep Kumar, 2020-10-16 By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students. |
| data analytics in education pdf: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like. |
| data analytics in education pdf: Transforming Learning with Meaningful Technologies Maren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, Jan Schneider, 2019-09-09 This book constitutes the proceedings of the 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, held in Delft, The Netherlands, in September 2019. The 41 research papers and 50 demo and poster papers presented in this volume were carefully reviewed and selected from 149 submissions. The contributions reflect the debate around the role of and challenges for cutting-edge 21st century meaningful technologies and advances such as artificial intelligence and robots, augmented reality and ubiquitous computing technologies and at the same time connecting them to different pedagogical approaches, types of learning settings, and application domains that can benefit from such technologies. |
| data analytics in education pdf: Data Mining and Predictive Analytics Daniel T. Larose, 2015-02-19 Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives. |
| data analytics in education pdf: Classification, (big) Data Analysis and Statistical Learning Francesco Mola, Claudio Conversano, Maurizio Vichi, 2018 This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8-10, 2015. |
| data analytics in education pdf: Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) Tutut Herawan, Mustafa Mat Deris, Jemal Abawajy, 2013-12-14 The proceeding is a collection of research papers presented at the International Conference on Data Engineering 2013 (DaEng-2013), a conference dedicated to address the challenges in the areas of database, information retrieval, data mining and knowledge management, thereby presenting a consolidated view to the interested researchers in the aforesaid fields. The goal of this conference was to bring together researchers and practitioners from academia and industry to focus on advanced on data engineering concepts and establishing new collaborations in these areas. The topics of interest are as follows but are not limited to: • Database theory • Data management • Data mining and warehousing • Data privacy & security • Information retrieval, integration and visualization • Information system • Knowledge discovery in databases • Mobile, grid and cloud computing • Knowledge-based • Knowledge management • Web data, services and intelligence |
| data analytics in education pdf: 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. |
| data analytics in education pdf: 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. |
Analytics for Education
"Educational data mining (EDM) develops methods and applies techniques from statistics, machine learning, and data mining to analyze data collected during teaching and learning. …
Data Analytics in Higher Education: An Integrated View - JISE
In an effort to continue discussions to establish a common view of analytics in higher education, this paper proposes a comprehensive framework for data analytics in higher education that …
Data Science & Big Data Analytics - download.e-bookshelf.de
The course, “Data Science and Big Data Analytics,” has become well accepted across academia and the industry. Led by EMC Education Services, this book is the result of efforts and …
Education big data and learning analytics: a bibliometric …
Education in big data and learning analytics are two important processes that produce impactful results and understanding. it is crucial to take advantage of these processes to enhance the...
Data Analytics and the Imperatives for Data- Informed …
book seek to explain and illustrate how data analytics can support a data-informed decision-making culture in higher education. While the focus of discussions in this book relate to data …
Introduction to Data Analysis Handbook - ed
in Section V of the Handbook we examine data analysis using examples of data from each of the Head Start content areas. We explore examples of how data analysis could be
13 Learning Analytics and Educational Data Mining
In recent years, the use of analytics and data mining – methodologies that extract useful and actionable information from large datasets – has become commonplace in science (i.e. Jing et …
Learning Analytics and Big Data in Higher Education
Learning analytics analyses both static and dynamic data extracted from teaching - learning environment to allow timely interventions by educator. It paves way for optimizing the learning …
Reuters Big Data for Education: Data Mining, Data Analytics
Data mining and data analytic software can provide immediate feedback to students and teachers about academic performance. That approach can analyze underlying patterns in order to …
WORKING PAPER | JUNE2020 Strategic Data Use in Higher …
improve the use of data analytics in higher education through its most important asset—people. INVESTIGATION OF HIGHER EDUCATION DATA NEEDS After a decade of supporting data …
Using Data to Improve Educational Outcomes
Why Is Data Use Important? The use of data is a powerful tool to strengthen academic outcomes for all students—especially underserved students. Using data tells us whether certain groups …
Big data analytics in higher education - IACIS
The main purpose of this paper is to analyze and discuss the existing studies of big data analytics in higher education and recognize the main areas of its usage and opportunities, while putting …
U.S. Department of Education Data Strategy
the full potential of data to improve education outcomes and leading the nation in a new era of evidence-based policy insights and data-driven operations. As an intermediate step, this strategy
The Power of Data for Decision Making and the Appropriate …
Data helps decision makers evaluate alternatives, make resource allocations, and make informed choices. The effective and ethical use of analytics can improve the academic and support …
Data for Students: The Potential of Data and Analytics for …
In this brief, I discuss the potential of new data sources in postsecondary education for data analytics and data science focused on student success, with the aim of sharing this knowledge …
Data Analytics Tools in Higher Education - CEUR-WS.org
The paper presents an analytical overview of 35 data analytics tools implemented and developed by higher education institutions. 2 Data analytics tools The AWE [1] is an early alert engine …
Learning Analytics Methods, Benefits, and Challenges in …
Educational data mining focuses on developing and implementing methods with a goal of promoting discoveries from data in educational settings. It examines patterns in a large data …
Enhancing Teaching and Learning Through Educational Data …
help education policymakers and administrators understand how data mining and analytics work and how they can be applied within online learning systems to support education-related …
BIG DATA & LEARNING ANALYTICS: A POTENTIAL WAY TO …
Analytics and big data have a significant role to play in the future of higher education. This paper attempts an analytical practice about the use of e-learning technological
Analytics for Education
"Educational data mining (EDM) develops methods and applies techniques from statistics, machine learning, and data mining to analyze data collected during teaching and learning. …
Data Analytics in Higher Education: An Integrated View - JISE
In an effort to continue discussions to establish a common view of analytics in higher education, this paper proposes a comprehensive framework for data analytics in higher education that …
Data Science & Big Data Analytics - download.e-bookshelf.de
The course, “Data Science and Big Data Analytics,” has become well accepted across academia and the industry. Led by EMC Education Services, this book is the result of efforts and …
Strategic Data Use in Higher Education - Harvard University
If higher education is to take advantage of data analytics to improve student outcomes and increase organizational effectiveness, it will have to find better ways to attract, train, and retain …
Education big data and learning analytics: a bibliometric …
Education in big data and learning analytics are two important processes that produce impactful results and understanding. it is crucial to take advantage of these processes to enhance the...
Data Analytics and the Imperatives for Data- Informed …
book seek to explain and illustrate how data analytics can support a data-informed decision-making culture in higher education. While the focus of discussions in this book relate to data …
Introduction to Data Analysis Handbook - ed
in Section V of the Handbook we examine data analysis using examples of data from each of the Head Start content areas. We explore examples of how data analysis could be
13 Learning Analytics and Educational Data Mining
In recent years, the use of analytics and data mining – methodologies that extract useful and actionable information from large datasets – has become commonplace in science (i.e. Jing et …
Learning Analytics and Big Data in Higher Education
Learning analytics analyses both static and dynamic data extracted from teaching - learning environment to allow timely interventions by educator. It paves way for optimizing the learning …
Reuters Big Data for Education: Data Mining, Data Analytics …
Data mining and data analytic software can provide immediate feedback to students and teachers about academic performance. That approach can analyze underlying patterns in order to …
WORKING PAPER | JUNE2020 Strategic Data Use in Higher …
improve the use of data analytics in higher education through its most important asset—people. INVESTIGATION OF HIGHER EDUCATION DATA NEEDS After a decade of supporting data …
Using Data to Improve Educational Outcomes
Why Is Data Use Important? The use of data is a powerful tool to strengthen academic outcomes for all students—especially underserved students. Using data tells us whether certain groups …
Big data analytics in higher education - IACIS
The main purpose of this paper is to analyze and discuss the existing studies of big data analytics in higher education and recognize the main areas of its usage and opportunities, while putting …
U.S. Department of Education Data Strategy
the full potential of data to improve education outcomes and leading the nation in a new era of evidence-based policy insights and data-driven operations. As an intermediate step, this strategy
The Power of Data for Decision Making and the Appropriate …
Data helps decision makers evaluate alternatives, make resource allocations, and make informed choices. The effective and ethical use of analytics can improve the academic and support …
Data for Students: The Potential of Data and Analytics for …
In this brief, I discuss the potential of new data sources in postsecondary education for data analytics and data science focused on student success, with the aim of sharing this knowledge …
Data Analytics Tools in Higher Education - CEUR-WS.org
The paper presents an analytical overview of 35 data analytics tools implemented and developed by higher education institutions. 2 Data analytics tools The AWE [1] is an early alert engine …
Learning Analytics Methods, Benefits, and Challenges in …
Educational data mining focuses on developing and implementing methods with a goal of promoting discoveries from data in educational settings. It examines patterns in a large data …
Enhancing Teaching and Learning Through Educational Data …
help education policymakers and administrators understand how data mining and analytics work and how they can be applied within online learning systems to support education-related …
BIG DATA & LEARNING ANALYTICS: A POTENTIAL WAY …
Analytics and big data have a significant role to play in the future of higher education. This paper attempts an analytical practice about the use of e-learning technological
