Intelligent Data-Centric Systems Sensor Collected Intelligence: Intelligent Data Analysis for E-Learning : Enhancing Security and Trustworthiness in Online Learning Systems EPUB, PDF, DOC
9780128045350 0128045353 "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", Intelligent Data Analysis for e-Learning 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. Intelligent Data Analysis for e-Learning provides functional approaches of trustworthiness analysis, modeling, assessment and prediction for stronger security and support in on-line learning. Intelligent Data Analysis for e-Learning highlights the security deficiencies found in most online collaborative learning systems. The book explores the 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, and 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 the security in e-learning systems. Provides guidelines for anomaly detection, security analysis, and trustworthiness data processing. Incorporates state-of-the-art multidisciplinary research on on-line collaborative learning, social networks, information security, learning management systems, and trustworthiness prediction. Proposes a parallel processing approach that decreases expensive data processing time. Offers strategies for ensuring against unfair and dishonest assessments. Demonstrates solutions using a real-life e-learning context.
9780128045350 0128045353 "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", Intelligent Data Analysis for e-Learning 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. Intelligent Data Analysis for e-Learning provides functional approaches of trustworthiness analysis, modeling, assessment and prediction for stronger security and support in on-line learning. Intelligent Data Analysis for e-Learning highlights the security deficiencies found in most online collaborative learning systems. The book explores the 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, and 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 the security in e-learning systems. Provides guidelines for anomaly detection, security analysis, and trustworthiness data processing. Incorporates state-of-the-art multidisciplinary research on on-line collaborative learning, social networks, information security, learning management systems, and trustworthiness prediction. Proposes a parallel processing approach that decreases expensive data processing time. Offers strategies for ensuring against unfair and dishonest assessments. Demonstrates solutions using a real-life e-learning context.