Big Data and Learning Analytics in Higher Education Current Theory a
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 p
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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 DOI 10.1007/978-3-319-06520-5
(eBook)
Library of Congress Control Number: 2016947402 © Springer International Publishing Switzerland 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms 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 specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The 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
Educational data science (EDS) is an emergent interdisciplinary field of inquiry, which brings together computer science, education, statistics, and other social sciences to examine and understand social and technical phenomena. EDS researchers and practitioners utilize various sets of procedures and techniques to gather, organize, manipulate, and interpret rich educational data sources. EDS also presents techniques for merging voluminous and diverse data sources together, ensuring consistency of these data sets, and creating unified visualizations to aid in understanding of complex data. Further, in this field, educational data scientists build mathematical models and use them to communicate insights/findings to other educational 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 field of research is concerned with developing methods for exploring i
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