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Autor(en): 
  • Santi Caballé
  • Jorge Miguel
  • Xhafa Fatos
  • Intelligent Data Analysis for E-Learning: Enhancing Security and Trustworthiness in Online Learning Systems 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  August 2016  
    Genre:  EDV / Informatik 
     
    assessment analytics / Cognitive e-assessment / COMPUTERS / Data Science / Data Visualization / COMPUTERS / Database Administration & Management / COMPUTERS / Security / General / COMPUTERS / Security / Network Security / CSCL and Mobile CSCL / Data Capture & Analysis
    ISBN:  9780128045350 
    EAN-Code: 
    9780128045350 
    Verlag:  Elsevier 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Intelligent Data-Centric Systems: Sensor Collected Intelligence  
    Dimensionen:  H 235 mm / B 191 mm / D 12 mm 
    Gewicht:  430 gr 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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