Intelligent Data Analysis for e-Learning Enhancing Security and Trustworthiness in Online Learning Systems Intelligent Data-Centric Systems Series
Auteurs : Miguel Jorge, Caballé Santi, Xhafa Fatos
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
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
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.
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
- 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
Date de parution : 08-2016
Ouvrage de 192 p.
19x23.3 cm
Thèmes d’Intelligent Data Analysis for e-Learning :
Mots-clés :
On-line collaborative learning; CSCL and Mobile CSCL; Networking; Learning Management Systems; Security for e-Learning; Information security technologies; Trustworthiness assessment; Trustworthiness models and methodologies; Trustworthiness prediction; Formative assessment for e-Learning; Assessment Analytics; Cognitive e-assessment; Massive data processing for effective trustworthiness; Data visualization for trustworthiness
