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- List of Figures
- List of Tables
- Chapter 1: Introduction
- 1.1 Objectives
- 1.2 Book Organization
- 1.3 Book Reading
- Chapter 2: Security for e-Learning
- 2.1 Background
- 2.2 Information Security in e-Learning
- 2.3 Secure Learning Management Systems
- 2.4 Security for e-Learning Paradigms
- 2.5 Discussion
- Chapter 3: Trustworthiness for secure collaborative learning
- 3.1 Background
- 3.2 Knowledge Management for Trustworthiness e-Learning Data
- 3.3 Trustworthiness-Based CSCL
- 3.4 Trustworthiness-Based Security for P2P e-Assessment
- 3.5 An e-Exam Case Study
- Chapter 4: Trustworthiness modeling and methodology for secure peer-to-peer e-Assessment
- 4.1 Trustworthiness Modeling
- 4.2 Trustworthiness-Based Security Methodology
- 4.3 Knowledge Management for Trustworthiness and Security Methodology
- 4.4 Building Student Profiles in e-Assessment
- 4.5 Case Study: Authentication for MOOC Platforms
- Chapter 5: Massive data processing for effective trustworthiness modeling
- 5.1 Overview on Parallel Processing
- 5.2 Parallel Massive Data Processing
- 5.3 The MapReduce Model and Hadoop
- 5.4 Massive Processing of Learning Management System Log Files
- 5.5 Application of the Massive Data Processing Approach
- 5.6 Discussion
- Chapter 6: Trustworthiness evaluation and prediction
- 6.1 e-Learning Context
- 6.2 Trustworthiness Evaluation
- 6.3 Trustworthiness Prediction
- Chapter 7: Trustworthiness in action: Data collection, processing, and visualization methods for real online courses
- 7.1 Data Collection and Processing Methods
- 7.2 MapReduce Approach Implementation
- 7.3 Peer-to-Peer Data Analysis and Visualization
- Chapter 8: Conclusions and future research work
- 8.1 Conclusions and lessons learned
- 8.2 Challenges and future research work
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
- Provides guidelines for anomaly detection, security analysis, and trustworthiness of data processing
- Incorporates state-of-the-art, multidisciplinary research on online collaborative learning, social networks, information security, learning management systems, and trustworthiness prediction
- Proposes a parallel processing approach that decreases the cost of expensive data processing
- Offers strategies for ensuring against unfair and dishonest assessments
- Demonstrates solutions using a real-life e-Learning context
IT researchers and practitioners, upper level and graduate students in computer science
- No. of pages:
- © Academic Press 2017
- 9th August 2016
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Jorge Miguel teaches operative systems and security in information systems and is in charge of San Jorge University’s Department of Information Systems.
Department of Information Systems, San Jorge University, Spain
Dr. Santi Caballé is an Associate Professor of Computer Science at the Open University of Catalonia (UOC). His research interests are Software Engineering and Web-applications for collaborative Work. He has presented at over 100 well-established international conferences and workshops such as the 6th IEEE International Conference on Intelligent Networking and Collaborative Systems and the Fourth International Workshop on Adaptive Learning via Interactive, Collaborative and Emotional Approaches (ALICE 2014) As part of his work in collaborative learning and computational intelligence he has edited nine books and dozens of journal articles.
Associate Professor of Computer Science, Open University of Catalonia (UOC), Spain
Fatos Xhafa received his PhD in Computer Science in 1998 from the Department of Computer Science of the Technical University of Catalonia (UPC), Barcelona, Spain. Currently, he holds a permanent position of Professor Titular at UPC, BarcelonaTech. He was a Visiting Professor at Birkbeck College, University of London (UK) during academic year 2009-2010 and Research Associate at Drexel University, Philadelphia (USA) during academic term 2004/2005. Dr. Xhafa has widely published in peer reviewed international journals, conferences/workshops, book chapters and edited books and proceedings in the field (http://dblp.uni-trier.de/pers/hd/x/Xhafa:Fatos). He is awarded teaching and research merits by Spanish Ministry of Science and Education. Dr. Xhafa has an extensive editorial and reviewing service. He is editor in Chief of International Journal of Grid and Utility Computing and International Journal of Space-based and Situated Computing from Inderscience. He is actively participating in the organization of several international conferences and workshops. His research interests include parallel and distributed algorithms, optimization, networking, P2P and Cloud computing, security and trustworthy computing, among others. He can be reached at email@example.com and more information can be found at http://www.cs.upc.edu/~fatos/
Technical University of Catalonia (UPC), Barcelona, Spain
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