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: Caballé, Santi
    role:
      role term: author.
  name:
    name part: Xhafa, Fatos
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2016
    edition: 1st edition
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (192 pages)
  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. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS To learn more about Elsevier's Series, Intelligent Data-Centric Systems, please visit this link: https://www.elsevier.com/books-and-journals/book-series/intelligent-data-centric-systems-sensor-collected-intelligence 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…
  note: Miguel, Jorge.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2017.
  note: Mode of access: World Wide Web.
  note: Made available through: Safari, an O'Reilly Media Company.
  identifier: 9780128045459
  identifier: 9780128045350
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 101116
    record change date: 20260128152757.6
    record identifier: in00004560098