Intelligent Data Analysis for e-Learning

Intelligent Data Analysis for e-Learning

Enhancing Security and Trustworthiness in Online Learning Systems

1st Edition - August 9, 2016

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  • Authors: Jorge Miguel, Santi Caballé, Fatos Xhafa
  • Paperback ISBN: 9780128045350
  • eBook ISBN: 9780128045459

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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

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

Product details

  • No. of pages: 192
  • Language: English
  • Copyright: © Academic Press 2016
  • Published: August 9, 2016
  • Imprint: Academic Press
  • Paperback ISBN: 9780128045350
  • eBook ISBN: 9780128045459

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 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 Caballé 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.

Affiliations and Expertise

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

Fatos Xhafa

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 Please visit also and at

Affiliations and Expertise

Full Professor of Computer Science, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

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