
Intelligent Data Analysis for e-Learning : Enhancing Security and Trustworthiness in Online Learning Systems
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Description
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
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Product details
- Paperback | 192 pages
- 191 x 235 x 10.41mm | 430g
- 01 Sep 2016
- Elsevier Science Publishing Co Inc
- Academic Press Inc
- San Diego, United States
- English
- 0128045353
- 9780128045350
- 1,530,241
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Table of contents
Chapter 2: Security for e-Learning
Chapter 3: Trustworthiness for secure collaborative learning
Chapter 4: Trustworthiness modeling and methodology for secure peer-to-peer e-Assessment
Chapter 5: Massive data processing for effective trustworthiness modeling
Chapter 6: Trustworthiness evaluation and prediction
Chapter 7: Trustworthiness in action: Data collection, processing, and visualization methods for real online courses
Chapter 8: Conclusions and future research work
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About Miguel Jorge
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