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Intelligent Data Analysis for e-Learning : Enhancing Security and Trustworthiness in Online Learning Systems.
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 marke...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Francisco, UNITED STATES :
Academic Press,
2016.
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| Colección: | Intelligent data-centric systems
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.