Intelligent Data Analysis for e-Learning : Enhancing Security and Trustworthiness in Online Learning Systems.
(eBook)
Author
Published
San Francisco, UNITED STATES : Academic Press, 2016.
Format
eBook
ISBN
0128045450, 9780128045459
Physical Desc
1 online resource (194)
Status
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Language
English
Notes
Description
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.
Description
AssessmentsDemonstrates solutions using a real-life e-Learning context.
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Citations
APA Citation, 7th Edition (style guide)
Miguel, J. (2016). Intelligent Data Analysis for e-Learning: Enhancing Security and Trustworthiness in Online Learning Systems . Academic Press.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Miguel, Jorge. 2016. Intelligent Data Analysis for E-Learning: Enhancing Security and Trustworthiness in Online Learning Systems. Academic Press.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Miguel, Jorge. Intelligent Data Analysis for E-Learning: Enhancing Security and Trustworthiness in Online Learning Systems Academic Press, 2016.
MLA Citation, 9th Edition (style guide)Miguel, Jorge. Intelligent Data Analysis for E-Learning: Enhancing Security and Trustworthiness in Online Learning Systems Academic Press, 2016.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
74571826-8fc3-5615-ebd3-106e4170ab42-eng
Grouping Information
| Grouped Work ID | 74571826-8fc3-5615-ebd3-106e4170ab42-eng |
|---|---|
| Full title | intelligent data analysis for e learning enhancing security and trustworthiness in online learning systems |
| Author | miguel jorge |
| Grouping Category | book |
| Last Update | 2024-09-06 16:31:08PM |
| Last Indexed | 2024-09-28 03:33:44AM |
Marc Record
| First Detected | Jul 29, 2024 04:02:34 PM |
|---|---|
| Last File Modification Time | Sep 06, 2024 04:37:15 PM |
MARC Record
| LEADER | 06566cam a2200517Ma 4500 | ||
|---|---|---|---|
| 001 | ocn958454794 | ||
| 003 | OCoLC | ||
| 005 | 20240830103855.0 | ||
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| 037 | |a 953482|b MIL | ||
| 037 | |a 9780128045459|b O'Reilly Media | ||
| 040 | |a IDEBK|b eng|e pn|c IDEBK|d YDX|d EBLCP|d OCLCQ|d GBVCP|d LVT|d OCLCQ|d UKMGB|d OCLCO|d OCLCF|d ORMDA|d OCLCO|d OCLCQ|d OCLCO | ||
| 049 | |a FMGA | ||
| 050 | 4 | |a LB2846.M548 2017 | |
| 082 | 0 | 4 | |a 005.8|2 23 |
| 100 | 1 | |a Miguel, Jorge. | |
| 245 | 1 | 0 | |a Intelligent Data Analysis for e-Learning :|b Enhancing Security and Trustworthiness in Online Learning Systems. |
| 260 | |a San Francisco, UNITED STATES :|b Academic Press,|c 2016. | ||
| 300 | |a 1 online resource (194) | ||
| 336 | |a text|b txt|2 rdacontent | ||
| 337 | |a computer|b c|2 rdamedia | ||
| 338 | |a online resource|b cr|2 rdacarrier | ||
| 490 | 0 | |a Intelligent data-centric systems | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 520 | 8 | |a AssessmentsDemonstrates solutions using a real-life e-Learning context. | |
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Information technology|x Management.|0 http://id.loc.gov/authorities/subjects/sh2008006980 | |
| 650 | 0 | |a Computer security|x Management. | |
| 650 | 0 | |a Computer-assisted instruction|x Security measures. | |
| 776 | 0 | 8 | |i Print version:|a Miguel, Jorge.|t Intelligent Data Analysis for e-Learning.|d San Francisco, UNITED STATES : Academic Press, 2016|z 9780128045350|z 0128045353|w (OCoLC)950449922 |
| 856 | 4 | 0 | |u https://www.aclib.us/OReilly |