​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​.

Webber, Henry Y. Zheng, Ying Zhou

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.

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.

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.

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.

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!

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.

This book describes technology used for effective sensing of our physical world and intelligent processing techniques for sensed information, which are essential to the success of Internet of Things (IoT). The authors provide a multidisciplinary view of sensor technology from materials, process, circuits, to big data domains and they showcase smart sensor systems in real applications including smart home, transportation, medical, environmental, agricultural, etc. Unlike earlier books on sensors, this book provides a “global” view on smart sensors covering abstraction levels from device, circuit, systems, and algorithms.

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.