Intelligent Data Analysis for e-Learning

Enhancing Security and Trustworthiness in Online Learning Systems

Nonfiction, Computers, Advanced Computing, Management Information Systems, Database Management, Reference & Language, Education & Teaching
Cover of the book Intelligent Data Analysis for e-Learning by Jorge Miguel, Santi Caballé, Fatos Xhafa, Elsevier Science
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Jorge Miguel, Santi Caballé, Fatos Xhafa ISBN: 9780128045459
Publisher: Elsevier Science Publication: September 6, 2016
Imprint: Academic Press Language: English
Author: Jorge Miguel, Santi Caballé, Fatos Xhafa
ISBN: 9780128045459
Publisher: Elsevier Science
Publication: September 6, 2016
Imprint: Academic Press
Language: English

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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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

More books from Elsevier Science

Cover of the book Elephant Sense and Sensibility by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Next Generation Biomonitoring: Part 1 by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Psychology of Learning and Motivation by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book WAIS-IV, WMS-IV, and ACS by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Analytical Profiles of Drug Substances and Excipients by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book The Theory of Gambling and Statistical Logic by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Working with Dynamic Crop Models by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Primer to the Immune Response by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Handbook of Defense Economics by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Air and Spaceborne Radar Systems by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Heterocyclic Chemistry in the 21st Century: A Tribute to Alan Katritzky by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Stress Testing and Risk Integration in Banks by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Materials Selection in Mechanical Design by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Shale Oil and Gas Handbook by Jorge Miguel, Santi Caballé, Fatos Xhafa
Cover of the book Principles and Applications of RF/Microwave in Healthcare and Biosensing by Jorge Miguel, Santi Caballé, Fatos Xhafa
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy