• DocumentCode
    2870955
  • Title

    Enhanced Understand of Biological Systems Using Structure-Behavior-Function Models

  • Author

    Helms, Michael ; Vattam, Swaroop ; Goel, Ashok K. ; Yen, Jeannette

  • Author_Institution
    Design & Intell. Lab., Georgia Inst. of Technol., Atlanta, GA, USA
  • fYear
    2011
  • fDate
    6-8 July 2011
  • Firstpage
    239
  • Lastpage
    243
  • Abstract
    An important issue in teaching interdisciplinary biologically inspired design is the external representations we use to foster understanding of biological systems. In this study we explore if functional models of biological systems, and in particular Structure-Behavior-Function (SBF) models, enable humans to better understand complex biological systems. The study compares the use of SBF models in answering questions about biological systems versus the use of textual, tabular and graphical representations. The results indicate that while no one representation is best for answering all types of questions, SBF models enable more accurate answers to questions entailing abstract and complex inferences.
  • Keywords
    behavioural sciences computing; biology computing; biomedical education; computer aided instruction; educational courses; inference mechanisms; psychology; question answering (information retrieval); teaching; SBF models; answering questions; complex biological systems; complex inferences; graphical representations; interdisciplinary biologically inspired design; structure behavior function models; tabular representations; teaching; textual representations; Artificial intelligence; Biological system modeling; Biological systems; Computational modeling; Mathematical model; Surface contamination; biological systems; functional models; learning; question answering; understanding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on
  • Conference_Location
    Athens, GA
  • ISSN
    2161-3761
  • Print_ISBN
    978-1-61284-209-7
  • Electronic_ISBN
    2161-3761
  • Type

    conf

  • DOI
    10.1109/ICALT.2011.76
  • Filename
    5992333