Big Data and Learning Analytics in Higher Education: Current Theory and Practice PDF
Preview Big Data and Learning Analytics in Higher Education: Current Theory and Practice
Ben Kei Daniel Editor Big Data and Learning Analytics in Higher Education Current Theory and Practice Big Data and Learning Analytics in Higher Education Ben Kei Daniel Editor Big Data and Learning Analytics in Higher Education Current Theory and Practice Editor Ben Kei Daniel University of Otago Dunedin , New Zealand ISBN 978-3-319-06519-9 ISBN 978-3-319-06520-5 (eBook) DOI 10.1007/978-3-319-06520-5 Library of Congress Control Number: 2016947402 © Springer International Publishing Switzerland 2017 T his work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfi lms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specifi c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. T he publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland This book is dedicated to one of my brothers, Mr. Lowo Joseph Daniel, who is thrilled with the idea of big data and analytics. I hope this book will inspire you Lowo to follow your dreams along this route. Foreword E ducational data science (EDS) is an emergent interdisciplinary fi eld of inquiry, which brings together computer science, education, statistics, and other social sci- ences to examine and understand social and technical phenomena. EDS researchers and practitioners utilize various sets of procedures and techniques to gather, orga- nize, manipulate, and interpret rich educational data sources. EDS also presents techniques for merging voluminous and diverse data sources together, ensuring con- sistency of these data sets, and creating unifi ed visualizations to aid in understand- ing of complex data. Further, in this fi eld, educational data scientists build mathematical models and use them to communicate insights/fi ndings to other edu- cational specialists and scientists in their team and if required to nonexpert stakeholders. As a subdiscipline of data science, EDS originated from discussions held during several workshops between years 2000 and 2007, mainly from the Educational Data Mining (EDM) Conference in 2008. EDM itself as a fi eld of research is concerned with developing methods for exploring increasingly large-scale educational data, to better understand students and the settings in which they learn. In the last years, other two international conferences were held, focusing on EDS themes. Learning and Knowledge Analytics (LAK2011) was the fi rst conference, followed by Learning at Scale (L@S) in 2014. Lately, conferences in this area focus discourse on exploring the impact of big data and learning analytics in fostering learning and teaching and in engaging the growing community of researchers, prac- titioners, and learners within higher education to build tools, procedures, and tech- niques to explore and solve complex learning problems. T his book introduces the reader to two current topics in EDS, big data and learn- ing analytics. Learning analytics examines the challenges of collecting, analyzing, and reporting data with the specifi c intent of improving student learning. Big data (BD), on the other hand, offers the potential to tackle a wide range of issues that appear when collecting and working with a large volume, variety, and velocity of data. Specifi cally, applying BD in education or learning at scale enables researchers to work with large numbers of students, where “large” is preferably thousands of students but can also apply to hundreds in in-person settings. vii viii Foreword T his book contains 15 chapters written by 32 international authors. It is orga- nized into two parts: big data and learning analytics. Chapters cover both introduc- tion, theory, limitations, methods, techniques, ethical considerations, recent trends, future research, case studies, and examples. They provide a comprehensive and full understanding of the current state of big data and learning analytics within higher education. The book is written in a simple style, making it accessible to under- graduate students and to other readers who might be interested in learning about big data and learning analytics within the realm of higher education. Because of the simplicity of presentation and illustrative nature of the issues presented, the book can serve both as an introductory text and as an advanced text for students, policy- makers, and researchers interested in exploring issues related to these two current hot topics of EDS. University of Cordoba Cristóbal Romero Córdoba , Spain Acknowledgments My research is funded by the Department of Higher Education Development Centre (HEDC), University of Otago, through the Performance-Based Research Fund (PBRF). I want to thank the university and the government of New Zealand for funding my research. I want to thank all contributors for sharing their research. Thanks go to my colleague, Russell Butson, for contributing early ideas that led to the development and maturity of the book. I want to express my sincere apprecia- tion to the members of the Springer technical and editorial team, especially to Ms. Anitha Chellamuthu and Lakshmi Narayanan R of SPi Technologies for overseeing the project, as well as to the anonymous reviewers for their constructive insights that have signifi cantly helped in improving the quality of work presented in this book. Thanks go to my wife Michelle Daniel, family, and friends for their continu- ous support while working on the book. Finally, to all who have contributed or inspired me in one way or another, while working on the project, I thank you! Educational Technology Ben Kei Daniel Higher Education Development Centre (HEDC) University of Otago Dunedin , New Zealand ix
Description:The list of books you might like
