rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Intelligent Data Analysis for e-Learning
  name:
    name part: Miguel, Jorge
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Santi Caballé
    role:
      role term: author.
  name:
    name part: Fatos Xhafa
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  genre: Security.
  origin info:
    place:
      place term: xx
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: 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.
  note: Includes bibliographical references and index.
  subject:
    topic: Educational statistics
    topic: Data processing
  subject:
    topic: Statistique de l'éducation
    topic: Informatique
  subject:
    topic: EDUCATION
    topic: Essays
  subject:
    topic: EDUCATION
    topic: Organizations & Institutions
  subject:
    topic: EDUCATION
    topic: Reference
  subject:
    topic: Educational statistics
    topic: Data processing
  classification: LB1028.43 .M54 2017
  classification: 370.72/7
  related item:
    title info:
      title: Intelligent data centric systems
  identifier: 9780128045459
  identifier: 0128045450
  identifier: 9780128045350
  identifier: 0128045353
  identifier: 5278544119508403463 TotalBoox
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: FEM
    record creation date: 160906
    record change date: 20260327151421.8
    record identifier: in00005761364
    language of cataloging:
      language term: eng