Intelligent Data Analysis for e-Learning: Enhancing Security and Trustworthiness in Online Learning Systems: Intelligent Data-Centric Systems
Autor Jorge Miguel, Santi Caballé, Fatos Xhafaen Limba Engleză Paperback – 21 aug 2016
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
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Specificații
ISBN-13: 9780128045350
ISBN-10: 0128045353
Pagini: 192
Dimensiuni: 191 x 235 x 18 mm
Greutate: 0.35 kg
Editura: ELSEVIER SCIENCE
Seria Intelligent Data-Centric Systems
ISBN-10: 0128045353
Pagini: 192
Dimensiuni: 191 x 235 x 18 mm
Greutate: 0.35 kg
Editura: ELSEVIER SCIENCE
Seria Intelligent Data-Centric Systems
Cuprins
1. Introduction2. Security for e-Learning3. Trustworthiness for secure collaborative learning4. Trustworthiness modeling and methodology for secure peer-to-peer e-Assessment5. Massive data processing for effective trustworthiness modeling6. Trustworthiness evaluation and prediction7. Trustworthiness in action: Data collection, processing, and visualization methods for real online courses8. Conclusions and future research work