04051cam a2200589M 4500 ocn969013612 OCoLC 20190328114816.0 m o d cr ||||||||||| tz 160906s2016 xx o 000 0 eng d FEM eng FEM OCLCQ YDX OPELS TEFOD IDEBK OCLCF QCL UPM N$T GZM OCLCQ B@L D6H U3W 959273666 959329997 968010833 9780128045459 (electronic bk.) 0128045450 9780128045350 0128045353 (OCoLC)969013612 (OCoLC)959273666 (OCoLC)959329997 (OCoLC)968010833 LB1028.43 .M54 2017 EDU 042000 bisacsh EDU 036000 bisacsh EDU 024000 bisacsh 370.72/7 23 Miguel, Jorge, author. Intelligent data analysis for e-learning / [electronic resource] [Place of publication not identified] : Elsevier Science, 2016. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rda Intelligent data centric 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 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 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. Vendor-supplied metadata. Includes bibliographical references and index. Educational statistics Data processing. Educational statistics Data processing. fast (OCoLC)fst00903607 EDUCATION / Essays bisacsh EDUCATION / Organizations & Institutions bisacsh EDUCATION / Reference bisacsh Computer Technology. Nonfiction. Security. Santi Caball�e author. Fatos Xhafa, author. TotalBoox, distributor. TBX, distributor. Print version: 9780128045350 0128045353 Intelligent data centric systems. ScienceDirect http://www.sciencedirect.com/science/book/9780128045350 247430 247430