• DocumentCode
    2057504
  • Title

    GVIS: An Integrating Infrastructure for Adaptively Mashing up User Data from Different Sources

  • Author

    Mazzola, Luca ; Mazza, Riccardo

  • Author_Institution
    Inst. for Commun. Technol., Univ. of Lugano, Lugano, Switzerland
  • fYear
    2010
  • fDate
    26-29 July 2010
  • Firstpage
    68
  • Lastpage
    72
  • Abstract
    In this article we present an infrastructure for creating mash up visual representations of the user profile that combines data from different sources. We explored this approach in the context of Life Long Learning, where different platforms or services are often used to support the learning process. The system is highly configurable and adaptive: data sources, data aggregations, and visualizations can be configured on the fly by the administrative user without changing any part of the software, and have an adaptive behavior based on linear combination of conditions about user or system characteristics. The visual profiles produced can assume different graphical formats and can be bound to different data, automatically adapting to personal preferences, knowledge, and contexts. We applied our infrastructure to a set of federated Learning Management Systems, retrieving information from different sources and creating some indicators of the learning activity. The software we developed provides learners with adaptive indicators of the learning state, and allows instructors to monitor the progress of their learners.
  • Keywords
    computer aided instruction; data visualisation; GVIS; infrastructure integration; learning management systems; mash up visual representations; Adaptation model; Adaptive systems; Computational modeling; Context; Data mining; Software; Visualization; Adaptive Presentations; Data Mashup; HCI; TEL;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Visualisation (IV), 2010 14th International Conference
  • Conference_Location
    London
  • ISSN
    1550-6037
  • Print_ISBN
    978-1-4244-7846-0
  • Type

    conf

  • DOI
    10.1109/IV.2010.19
  • Filename
    5571358