| Description |
1 online resource (194) |
| Series |
Intelligent data-centric systems
|
| Contents |
Front Cover; Intelligent Data Analysis for e-Learning: Enhancing Security and Trustworthiness in Online Learning Systems; Copyright; Dedication; Contents; 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; 2.1 Background; 2.2 Information Security in e-Learning; 2.2.1 Classifying Security Attacks; 2.2.2 Security Attacks in e-Learning; 2.2.3 Modeling Security Services; 2.2.4 Security in e-Learning: Real e-Learning Scenarios. |
|
2.3 Secure Learning Management Systems2.4 Security for e-Learning Paradigms; 2.4.1 Collaborative Learning; 2.4.2 Mobile Learning; 2.4.3 Massive Open Online Courses; 2.5 Discussion; Chapter 3: Trustworthiness for secure collaborative learning; 3.1 Background; 3.1.1 General Trustworthiness Models; 3.1.2 Trustworthiness Factors and Rules; 3.1.3 Trustworthiness in e-Learning; 3.1.4 Normalized Trustworthiness Models; 3.1.5 Time Factor and Trustworthiness Sequences; 3.1.6 Predicting Trustworthiness; 3.1.7 Related Trustworthiness Methodological Approaches. |
|
3.2 Knowledge management for trustworthiness e-Learning data3.2.1 Knowledge Management Process; 3.2.2 Data Collection and Processing; 3.2.3 Educational Data Mining and Learning Analytics; 3.2.4 Data Visualization; 3.2.5 Data Analysis and Visualization for P2P Models; 3.3 Trustworthiness-based CSCL; 3.3.1 Security in CSCL Based on Trustworthiness; 3.3.2 Functional Security Approaches for CSCL; 3.3.3 Functional Security for CSCL Based on Trustworthiness; 3.4 Trustworthiness-based security for P2P e-Assessment; 3.4.1 Assessment Classification; 3.4.2 Security in e-Assessment. |
|
3.4.3 Secure P2P e-Assessment3.4.4 P2P e-Assessment and Social Networks; 3.4.5 Security Limitations and Discussion; 3.5 An e-Exam Case Study; Chapter 4: Trustworthiness modeling and methodology for secure peer-to-peer e-Assessment; 4.1 Trustworthiness Modeling; 4.1.1 Notation and Terminology; 4.1.2 Modeling Trustworthiness Levels and Indicators; 4.1.3 Student Activity Data Sources; 4.1.4 Data Normalization; 4.1.5 Modeling Normalized Trustworthiness Levels; 4.1.6 Pearson Correlation Analysis; 4.2 Trustworthiness-Based Security Methodology; 4.2.1 Theoretical Analysis. |
|
4.2.2 Methodology Key Phases4.2.3 Building Trustworthiness Components; 4.2.4 Trustworthiness Analysis and Data Processing; 4.2.5 Trustworthiness Evaluation and Prediction; 4.3 Knowledge Management for Trustworthiness and Security Methodology; 4.3.1 Data Collection Within Trustworthiness and Security Methodology; 4.3.2 Data Processing Within Trustworthiness and Security Methodology; 4.3.3 Data Analysis Within Trustworthiness and Security Methodology; 4.3.4 Data Visualization and Knowledge Discovery Within Trustworthiness and Security Methodology. |
| Summary |
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. Provides guidelines for anomaly detection, security analysis, and trustworthiness of data processingIncorporates state-of-the-art, multidisciplinary research on online collaborative learning, social networks, information security, learning management systems, and trustworthiness predictionProposes a parallel processing approach that decreases the cost of expensive data processing Offers strategies for ensuring against unfair and dishonest. |
|
AssessmentsDemonstrates solutions using a real-life e-Learning context. |
| Subject |
Information technology -- Management.
|
|
Computer security -- Management.
|
|
Computer-assisted instruction -- Security measures.
|
|
Technologie de l'information -- Gestion. |
|
Sécurité informatique -- Gestion. |
|
Enseignement assisté par ordinateur -- Sécurité -- Mesures. |
|
Computer security -- Management |
|
Information technology -- Management |
| Other Form: |
Print version: Miguel, Jorge. Intelligent Data Analysis for e-Learning. San Francisco, UNITED STATES : Academic Press, 2016 9780128045350 0128045353 (OCoLC)950449922 |
| ISBN |
0128045450 (ebk) |
|
9780128045459 |
|
More Resources