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| data analytics in education: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| data analytics in education: 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: Big Data on Campus Karen L. Webber, Henry Y. Zheng, 2020-11-03 Webber, Henry Y. Zheng, Ying Zhou |
| data analytics in education: 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: You Are a Data Person Amelia Parnell, 2023-07-03 Internal and external pressure continues to mount for college professionals to provide evidence of successful activities, programs, and services, which means that, going forward, nearly every campus professional will need to approach their work with a data-informed perspective.But you find yourself thinking “I am not a data person”.Yes, you are. Or can be with the help of Amelia Parnell.You Are a Data Person provides context for the levels at which you are currently comfortable using data, helps you identify both the areas where you should strengthen your knowledge and where you can use this knowledge in your particular university role.For example, the rising cost to deliver high-quality programs and services to students has pushed many institutions to reallocate resources to find efficiencies. Also, more institutions are intentionally connecting classroom and cocurricular learning experiences which, in some instances, requires an increased gathering of evidence that students have acquired certain skills and competencies. In addition to programs, services, and pedagogy, professionals are constantly monitoring the rates at which students are entering, remaining enrolled in, and leaving the institution, as those movements impact the institution’s financial position.From teaching professors to student affairs personnel and beyond, Parnell offers tangible examples of how professionals can make data contributions at their current and future knowledge level, and will even inspire readers to take the initiative to engage in data projects.The book includes a set of self-assessment questions and a companion set of action steps and available resources to help readers accept their identity as a data person. It also includes an annotated list of at least 20 indicators that any higher education professional can examine without sophisticated data analyses. |
| data analytics in education: 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: Formative Assessment, Learning Data Analytics and Gamification Santi Caballé, Robert Clarisó, 2016-05-10 Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student progress given the explosion of e-learning environments, such as MOOCs and online courses that incorporate activities such as design and modeling. This book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification. This data, when used effectively, can have a positive impact on learning environments and be used for building learner profiles, community building, and as a tactic to create a collaborative team. Using numerous illustrative examples and theoretical and practical results, leading international experts discuss application of automatic techniques for e-assessment of learning activities, methods to collect, analyze, and correctly visualize learning data in educational environments, applications, benefits and challenges of using gamification techniques in academic contexts, and solutions and strategies for increasing student participation and performance. - Discusses application of automatic techniques for e-assessment of learning activities - Presents strategies to provide immediate and useful feedback on students' activities - Provides methods to collect, analyze, and correctly visualize learning data in educational environments - Explains the applications, benefits, and challenges of using gamification techniques in academic contexts - Offers solutions to increase students' participation and performance while lowering drop-out rates and retention levels |
| data analytics in education: Advancing the Power of Learning Analytics and Big Data in Education Azevedo, Ana, Azevedo, José Manuel, Onohuome Uhomoibhi, James, Ossiannilsson, Ebba, 2021-03-19 The term learning analytics is used in the context of the use of analytics in e-learning environments. Learning analytics is used to improve quality. It uses data about students and their activities to provide better understanding and to improve student learning. The use of learning management systems, where the activity of the students can be easily accessed, potentiated the use of learning analytics to understand their route during the learning process, help students be aware of their progress, and detect situations where students can give up the course before its completion, which is a growing problem in e-learning environments. Advancing the Power of Learning Analytics and Big Data in Education provides insights concerning the use of learning analytics, the role and impact of analytics on education, and how learning analytics are designed, employed, and assessed. The chapters will discuss factors affecting learning analytics such as human factors, geographical factors, technological factors, and ethical and legal factors. This book is ideal for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in the use of big data and learning analytics for improved student success and educational environments. |
| data analytics in education: Big Data in Education Ben Williamson, 2017-07-24 Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment! |
| data analytics in education: Using Data in Schools to Inform Leadership and Decision Making Alex J. Bowers, Alan R. Shoho, Bruce G. Barnett, 2014-11-01 Our fifth book in the International Research on School Leadership series focuses on the use of data in schools and districts as useful information for leadership and decision making. Schools are awash in data and information, from test scores, to grades, to discipline reports, and attendance as just a short list of student information sources, while additional streams of data feed into schools and districts from teachers and parents as well as local, regional and national policy levels. To deal with the data, schools have implemented a variety of data practices, from data rooms, to data days, data walks, and data protocols. However, despite the flood of data, successful school leaders are leveraging an analysis of their school’s data as a means to bring about continuous improvement in an effort to improve instruction for all students. Nevertheless, some drown, some swim, while others find success. Our goal in this book volume is to bring together a set of chapters by authors who examine successful data use as it relates to leadership and school improvement. In particular, the chapters in this volume consider important issues in this domain, including: • How educational leaders use data to inform their practice. • What types of data and data analysis are most useful to successful school leaders. • To what extent are data driven and data informed practices helping school leaders positively change instructional practice? • In what ways does good data collection and analysis feed into successful continuous improvement and holistic systems thinking? • How have school leadership practices changed as more data and data analysis techniques have become available? • What are the major obstacles facing school leaders when using data for decision making and how do they overcome them? |
| data analytics in education: Data Analytics Applications in Education Jan Vanthienen, Kristof De Witte, 2017-09-29 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. |
| data analytics in education: Teaching Data Analytics Susan Vowels, Katherine Leaming Goldberg, 2019-06-17 The need for analytics skills is a source of the burgeoning growth in the number of analytics and decision science programs in higher education developed to feed the need for capable employees in this area. The very size and continuing growth of this need means that there is still space for new program development. Schools wishing to pursue business analytics programs intentionally assess the maturity level of their programs and take steps to close the gap. Teaching Data Analytics: Pedagogy and Program Design is a reference for faculty and administrators seeking direction about adding or enhancing analytics offerings at their institutions. It provides guidance by examining best practices from the perspectives of faculty and practitioners. By emphasizing the connection of data analytics to organizational success, it reviews the position of analytics and decision science programs in higher education, and to review the critical connection between this area of study and career opportunities. The book features: A variety of perspectives ranging from the scholarly theoretical to the practitioner applied An in-depth look into a wide breadth of skills from closely technology-focused to robustly soft human connection skills Resources for existing faculty to acquire and maintain additional analytics-relevant skills that can enrich their current course offerings. Acknowledging the dichotomy between data analytics and data science, this book emphasizes data analytics rather than data science, although the book does touch upon the data science realm. Starting with industry perspectives, the book covers the applied world of data analytics, covering necessary skills and applications, as well as developing compelling visualizations. It then dives into pedagogical and program design approaches in data analytics education and concludes with ideas for program design tactics. This reference is a launching point for discussions about how to connect industry’s need for skilled data analysts to higher education’s need to design a rigorous curriculum that promotes student critical thinking, communication, and ethical skills. It also provides insight into adding new elements to existing data analytics courses and for taking the next step in adding data analytics offerings, whether it be incorporating additional analytics assignments into existing courses, offering one course designed for undergraduates, or an integrated program designed for graduate students. |
| data analytics in education: Data Analysis for Continuous School Improvement Victoria L. Bernhardt, 2017-09-19 Data Analysis for Continuous School Improvement provides a new definition of school improvement, away from a singular focus on compliance, toward a true commitment to excellence. This book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of data. The updated edition provides a wealth of tools, protocols, timelines, examples, and strategies that will help schools and districts become genuine learning organizations. |
| data analytics in education: Learning Analytics in Education David Niemi, Roy D. Pea, Bror Saxberg, Richard E. Clark, 2018-08-01 This book provides a comprehensive introduction by an extraordinary range of experts to the recent and rapidly developing field of learning analytics. Some of the finest current thinkers about ways to interpret and benefit from the increasing amount of evidence from learners’ experiences have taken time to explain their methods, describe examples, and point out new underpinnings for the field. Together, they show how this new field has the potential to dramatically increase learner success through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings. Learning analytics is much more than “analyzing learning data”—it is about deeply understanding what learning activities work well, for whom, and when. Learning Analytics in Education provides an essential framework, as well as guidance and examples, for a wide range of professionals interested in the future of learning. If you are already involved in learning analytics, or otherwise trying to use an increasing density of evidence to understand learners’ progress, these leading thinkers in the field may give you new insights. If you are engaged in teaching at any level, or training future teachers/faculty for this new, increasingly technology-enhanced learning world, and want some sense of the potential opportunities (and pitfalls) of what technology can bring to your teaching and students, these forward-thinking leaders can spark your imagination. If you are involved in research around uses of technology, improving learning measurements, better ways to use evidence to improve learning, or in more deeply understanding human learning itself, you will find additional ideas and insights from some of the best thinkers in the field here. If you are involved in making administrative or policy decisions about learning, you will find new ideas (and dilemmas) coming your way from inevitable changes in how we design and deliver instruction, how we measure the outcomes, and how we provide feedback to students, teachers, developers, administrators, and policy-makers. For all these players, the trick will be to get the most out of all the new developments to efficiently and effectively improve learning performance, without getting distracted by “shiny” technologies that are disconnected from how human learning and development actually work. |
| data analytics in education: Data Mining and Learning Analytics Samira ElAtia, Donald Ipperciel, Osmar R. Zaïane, 2016-09-20 Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research. |
| data analytics in education: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data analytics in education: OECD Digital Education Outlook 2021 Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots OECD, 2021-06-08 How might digital technology and notably smart technologies based on artificial intelligence (AI), learning analytics, robotics, and others transform education? This book explores such question. It focuses on how smart technologies currently change education in the classroom and the management of educational organisations and systems. |
| data analytics in education: Street Data Shane Safir, Jamila Dugan, 2021-02-12 Radically reimagine our ways of being, learning, and doing Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on fixing and filling academic gaps, we must envision and rebuild the system from the student up—with classrooms, schools and systems built around students’ brilliance, cultural wealth, and intellectual potential. Street data reminds us that what is measurable is not the same as what is valuable and that data can be humanizing, liberatory and healing. By breaking down street data fundamentals: what it is, how to gather it, and how it can complement other forms of data to guide a school or district’s equity journey, Safir and Dugan offer an actionable framework for school transformation. Written for educators and policymakers, this book · Offers fresh ideas and innovative tools to apply immediately · Provides an asset-based model to help educators look for what’s right in our students and communities instead of seeking what’s wrong · Explores a different application of data, from its capacity to help us diagnose root causes of inequity, to its potential to transform learning, and its power to reshape adult culture Now is the time to take an antiracist stance, interrogate our assumptions about knowledge, measurement, and what really matters when it comes to educating young people. |
| data analytics in education: Learning Analytics Goes to School Andrew Krumm, Barbara Means, Marie Bienkowski, 2018-01-12 Learning Analytics Goes to School presents a framework for engaging in education research and improving education practice through the use of newly available data sources and analytical approaches. The application of data-intensive research techniques to understanding and improving learning environments has been growing at a rapid pace. In this book, three leading researchers convey lessons from their own experiences—and the current state of the art in educational data mining and learning analytics more generally—by providing an explicit set of tools and processes for engaging in collaborative data-intensive improvement. |
| data analytics in education: Learning Analytics in Higher Education Jaime Lester, Carrie Klein, Aditya Johri, Huzefa Rangwala, 2018-08-06 Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical, theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision-making, ways to effectively evaluate and research learning analytics, integration of learning analytics into practice, organizational barriers and opportunities for harnessing Big Data to create and support use of these tools, and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators. |
| data analytics in education: Driven by Data Paul Bambrick-Santoyo, 2010-04-12 Offers a practical guide for improving schools dramatically that will enable all students from all backgrounds to achieve at high levels. Includes assessment forms, an index, and a DVD. |
| data analytics in education: Maximizing Social Science Research Through Publicly Accessible Data Sets Perry, S. Marshall, 2017-10-31 Making research in all fields of study readily available is imperative in order to circulate new information and upcoming trends. This is possible through the efficient utilization of collections of information. Maximizing Social Science Research Through Publicly Accessible Data Sets is an essential reference source for the latest academic perspectives on a wide range of methodologies and large data sets with the purpose of enhancing research in the areas of human society and social relationships. Featuring coverage on a broad range of topics such as student achievement, teacher efficacy, and instructional leadership, this book is ideally designed for academicians, researchers, and practitioners seeking material on the availability and distribution methods of research content. |
| data analytics in education: The Analytics Revolution in Higher Education Jonathan S. Gagliardi, Amelia Parnell, Julia Carpenter-Hubin, 2023-07-03 Co-published with and In this era of “Big Data,” institutions of higher education are challenged to make the most of the information they have to improve student learning outcomes, close equity gaps, keep costs down, and address the economic needs of the communities they serve at the local, regional, and national levels. This book helps readers understand and respond to this “analytics revolution,” examining the evolving dynamics of the institutional research (IR) function, and the many audiences that institutional researchers need to serve.Internally, there is a growing need among senior leaders, administrators, faculty, advisors, and staff for decision analytics that help craft better resource strategies and bring greater efficiencies and return-on-investment for students and families. Externally, state legislators, the federal government, and philanthropies demand more forecasting and more evidence than ever before. These demands require new and creative responses, as they are added to previous demands, rather than replacing them, nor do they come with additional resources to produce the analysis to make data into actionable improvements. Thus the IR function must become that of teacher, ensuring that data and analyses are accurate, timely, accessible, and compelling, whether produced by an IR office or some other source. Despite formidable challenges, IR functions have begun to leverage big data and unlock the power of predictive tools and techniques, contributing to improved student outcomes. |
| data analytics in education: Statistics Made Simple for School Leaders Susan Rovezzi Carroll, David J. Carroll, 2002-10-16 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. |
| data analytics in education: Challenges and Applications of Data Analytics in Social Perspectives Sathiyamoorthi, V., Elci, Atilla, 2020-12-04 With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students. |
| data analytics in education: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie L. Terrell, 2018-05-23 |
| data analytics in education: Innovative Learning Analytics for Evaluating Instruction Theodore W. Frick, Rodney D. Myers, Cesur Dagli, Andrew F. Barrett, 2021-07-19 Innovative Learning Analytics for Evaluating Instruction covers the application of a forward-thinking research methodology that uses big data to evaluate the effectiveness of online instruction. Analysis of Patterns in Time (APT) is a practical analytic approach that finds meaningful patterns in massive data sets, capturing temporal maps of students’ learning journeys by combining qualitative and quantitative methods. Offering conceptual and research overviews, design principles, historical examples, and more, this book demonstrates how APT can yield strong, easily generalizable empirical evidence through big data; help students succeed in their learning journeys; and document the extraordinary effectiveness of First Principles of Instruction. It is an ideal resource for faculty and professionals in instructional design, learning engineering, online learning, program evaluation, and research methods. |
| data analytics in education: 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: 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: 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: Research Anthology on Big Data Analytics, Architectures, and Applications Information Resources Management Association, 2022 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| data analytics in education: Data Analytics and Psychometrics Hong Jiao, Robert W. Lissitz, Anna Van Wie, 2018-12-01 The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large-scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student’s learning and guide teacher’s instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices. |
| data analytics in education: The Global Purpose Approach Tarece Johnson Ed.D., 2018-02-15 I created The Global Purpose Approach to help people gain the interest, knowledge, and skills needed for them to become more global-minded individuals. The idea for the book evolved from the need to enhance instruction in multicultural education so that people may be more knowledgeable, respectful, and appreciative of the geographies, languages, and cultures of people across the globe. The principles, content, and strategies that result comprise The Global Purpose Approachan instructional framework to introduce and integrate multiculturalism in schools, classrooms, private corporations, public agencies, and individuals daily lives. The purpose of The Global Purpose Approach is to foster multicultural awareness and education by helping to understand, respect, and appreciate similarities and differences across cultures. It consists of six guiding principlespurpose, environment, observation, perspective, language, and exploration (PEOPLE)together with an outline (NO FEAR) and resources for interactive and immersive learning of the core elements of culture. The intended impact of The Global Purpose Approach is to develop people who have enhanced knowledge and appreciation of diverse cultures and who can, therefore, interact and contribute innovatively in an interconnected, global society. Human resource professionals, organizational leaders, school districts, and teachers may use this book as a guide to integrate multicultural education in their departments, organizations, school systems, and classrooms. It also allows for educators to easily adapt the approach to fit their curriculum requirements and existing teaching plans and to implement it within their daily teaching. In this way, The Global Purpose Approach can be seen as a way to strengthen current teaching practices by adopting a set of philosophies and utilizing activities to foster more globally connected, engaged citizens without the need to fully revise or change. |
| data analytics in education: Big Data MBA Bill Schmarzo, 2015-12-11 Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions. |
| data analytics in education: 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: 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: Data Analytics in Medicine Information Resources Management Association, 2019-11-18 This book examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations-- |
| data analytics in education: 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: Educational Data Analytics for Teachers and School Leaders Sofia Mougiakou, Dimitra Vinatsella, Demetrios Sampson, Zacharoula Papamitsiou, Michail Giannakos, Dirk Ifenthaler, 2022-10-28 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 analytics in education: Data Science in Higher Education Jesse Lawson, 2015-09-06 Be the Change your Institution Needs What are leaders in research saying about Data Science in Higher Education? Where has this book been all these years? This is THE starting point for researchers looking for a leg up in today's college environment. Two parts discussion, one part methodology, and one part witty humor. I love it! Buy this book for your analysts. They and your college will thank you. This is the only book on data science specific for higher education research that covers both theory and practice. I'm not a programmer at all, and I found this book very enjoyable. You wont regret it -- I know I don't! When our department was tasked with coming up with a predictive 'machine-learning' model, we hired Jesse to help us. His charisma and knowledge are unmatched, and this book only helps to breathe fresh life into issues in research today that are all too often swept under the rug. Discover the tools to take your institution to the next level! Data Science in higher education is the process of turning raw institutional data into actionable intelligence. With this introduction to foundational topics in machine learning and predictive analytics, ambitious leaders in research can develop and employ sophisticated predictive models to better inform their institution's decision-making process. You don't need an advanced degree in math or statistics to do data science. With the open-source statistical programming language R, you'll learn how to tackle real-life institutional data challenges (with actual institutional data!) by going step-by-step through different case studies. Topics include: Simple, Multiple, & Logistic Regression Techniques, and Naive Bayes Classifiers Best Practices for Data Scientists in Higher Education Narrative-style stories, gotchas, and insights from actual data science jobs at colleges and universities Forget the textbooks. This is a book on data science written for institutional researchers *by* an institutional researcher. You need this book.------------------------------------------ Data Science is the art of carefully picking through that pile of book pages and putting together a complete book. It's the art of developing a narrative for your data, so that all the raw information that your institution warehouses and reports in bar charts and histograms is replaced with actionable intelligence. Here's what we know: Data science can and should be an integral part of college and university operations. Institutional effectiveness should be working side-by-side with faculty and educators to collect, clean, and mine through data of current and past students' behaviors in order to better empower counseling and advisement services (whether virtual or otherwise). Data itself should be considered an asset to an institution, and the data mining process a necessary function of institutional operations. So how do we do it? It starts with a solid perspective and great research tools. With Data Science in Higher Education you'll learn about and solve real-world institutional problems with open-source tools and machine learning research techniques. Using R, you'll tackle case studies from real colleges and develop predictive analytical solutions to problems that colleges and universities face to this day. |
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