Intelligent Data Analysis for e-Learning
Enhancing Security and Trustworthiness in Online Learning Systems
Published on 22. August 2016
Book
Paperback/Softback
192 pages
978-0-12-804535-0 (ISBN)
Description
Intelligent Data Analysis for e-Learning: Enhancing Security and Trustworthiness in Online Learning Systems addresses information security within e-Learning based on trustworthiness assessment and prediction. Over the past decade, many learning management systems have appeared in the education market. Security in these systems is essential for protecting against unfair and dishonest conduct-most notably cheating-however, e-Learning services are often designed and implemented without considering security requirements.
This book provides functional approaches of trustworthiness analysis, modeling, assessment, and prediction for stronger security and support in online learning, highlighting the security deficiencies found in most online collaborative learning systems. The book explores trustworthiness methodologies based on collective intelligence than can overcome these deficiencies. It examines trustworthiness analysis that utilizes the large amounts of data-learning activities generate. In addition, as processing this data is costly, the book offers a parallel processing paradigm that can support learning activities in real-time.
The book discusses data visualization methods for managing e-Learning, providing the tools needed to analyze the data collected. Using a case-based approach, the book concludes with models and methodologies for evaluating and validating security in e-Learning systems.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
This book provides functional approaches of trustworthiness analysis, modeling, assessment, and prediction for stronger security and support in online learning, highlighting the security deficiencies found in most online collaborative learning systems. The book explores trustworthiness methodologies based on collective intelligence than can overcome these deficiencies. It examines trustworthiness analysis that utilizes the large amounts of data-learning activities generate. In addition, as processing this data is costly, the book offers a parallel processing paradigm that can support learning activities in real-time.
The book discusses data visualization methods for managing e-Learning, providing the tools needed to analyze the data collected. Using a case-based approach, the book concludes with models and methodologies for evaluating and validating security in e-Learning systems.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
More details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Weight
430 gr
ISBN-13
978-0-12-804535-0 (9780128045350)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Jorge Miguel teaches operative systems and security in information systems and is in charge of San Jorge University's Department of Information Systems. Santi Caballe is a full professor at the Universitat Oberta de Catalunya (UOC) based in Barcelona, Spain. He holds a PhD, Master's, and Bachelor's in computing engineering from the UOC where he teaches on-line courses on software engineering and conducts research activity on the interdisciplinary field of learning engineering by combining e-learning, artificial intelligence, software engineering and distributed computing. He has over 250 peer-reviewed publications, including 15 books, 60 papers in indexed journals, and 150 conference papers. Professor Caballe has led and participated in over 30 national and international research projects and has been involved in the organization of many international research events. He also serves as editor for books and special issues of leading international journals. Fatos Xhafa, PhD in Computer Science, is Full Professor at the Technical University of Catalonia (UPC), Barcelona, Spain. He has held various tenured and visiting professorship positions. He was a Visiting Professor at the University of Surrey, UK (2019/2020), Visiting Professor at the Birkbeck College, University of London, UK (2009/2010) and a Research Associate at Drexel University, Philadelphia, USA (2004/2005). He was a Distinguished Guest Professor at Hubei University of Technology, China, for the duration of three years (2016-2019). Prof. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters, edited books and proceedings in the field (H-index 55). He has been awarded teaching and research merits by the Spanish Ministry of Science and Education, by IEEE conferences and best paper awards. Prof. Xhafa has an extensive editorial service. He is founder and Editor-In-Chief of Internet of Things - Journal - Elsevier (Scopus and Clarivate WoS Science Citation Index) and of International Journal of Grid and Utility Computing, (Emerging Sources Citation Index), and AE/EB Member of several indexed Int'l Journals. Prof. Xhafa is a member of IEEE Communications Society, IEEE Systems, Man & Cybernetics Society and Founder Member of Emerging Technical Subcommittee of Internet of Things.
His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at fatos@cs.upc.edu. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos
His research interests include IoT and Cloud-to-thing continuum computing, massive data processing and collective intelligence, optimization, security and trustworthy computing and machine learning, among others. He can be reached at fatos@cs.upc.edu. Please visit also http://www.cs.upc.edu/~fatos/ and at http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos
Content
1. Introduction2. Security for e-Learning3. Trustworthiness for secure collaborative learning4. Trustworthiness modeling and methodology for secure peer-to-peer e-Assessment5. Massive data processing for effective trustworthiness modeling6. Trustworthiness evaluation and prediction7. Trustworthiness in action: Data collection, processing, and visualization methods for real online courses8. Conclusions and future research work