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Intelligent Data Analysis for e-Learning - 1st Edition - ISBN: 9780128045350, 9780128045459

Intelligent Data Analysis for e-Learning

1st Edition

Enhancing Security and Trustworthiness in Online Learning Systems

Authors: Jorge Miguel Santi Caballé Fatos Xhafa
Paperback ISBN: 9780128045350
eBook ISBN: 9780128045459
Imprint: Academic Press
Published Date: 9th August 2016
Page Count: 192
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Table of Contents

  • Dedication
  • List of Figures
  • List of Tables
  • Foreword
  • Acknowledgments
  • Chapter 1: Introduction
    • 1.1 Objectives
    • 1.2 Book Organization
    • 1.3 Book Reading
  • Chapter 2: Security for e-Learning
    • Abstract
    • 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
    • Abstract
    • 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
    • Abstract
    • 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
    • Abstract
    • 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
    • Abstract
    • 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
    • Abstract
    • 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
    • Abstract
    • 8.1 Conclusions and lessons learned
    • 8.2 Challenges and future research work
  • Glossary
  • Bibliography
  • Index


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

Key Features

  • 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 2016
9th August 2016
Academic Press
Paperback ISBN:
eBook ISBN:

Ratings and Reviews

About the Authors

Jorge Miguel

Jorge Miguel teaches operative systems and security in information systems and is in charge of San Jorge University’s Department of Information Systems.

Affiliations and Expertise

Department of Information Systems, San Jorge University, Spain

Santi Caballé

Santi Caballé is a Full Professor of Learning Engineering at the Faculty of Computer Science, Multimedia and Telecommunications of the Universitat Oberta de Catalunya (UOC), based in Barcelona, Spain, where he conducts online teaching and research activity in the fields of Learning Engineering (eLearning) and Software Engineering. Prof. Caballé holds Bachelor’s, Master’s and Ph.D. degrees in Computing Engineering at the UOC, where he is currently the Head of the research group SmartLearn devoted to the intensive use of ICT for Education. Prof. Caballé has leaded and participated in over 30 research and teaching innovation projects, including leading European funded projects, and has been involved in the organization of many international research events. His publication record spans over 250 peer-reviewed publications, including 15 authored and edited books, 55 papers of indexed journals and over 150 conference papers. His research contributions are widely cited.

Affiliations and Expertise

Professor, Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Barcelona, Spain

Fatos Xhafa

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. He has been awarded teaching and research merits by Spanish Ministry of Science and Education. 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, and security and trustworthy computing.

Affiliations and Expertise

Professor, Technical University of Catalonia (UPC), Barcelona, Spain