• DocumentCode
    188662
  • Title

    Arg Teach - A Learning Tool for Argumentation Theory

  • Author

    Dauphin, Jeremie ; Schulz, Claudia

  • Author_Institution
    Dept. of Comput., Imperial Coll. London, London, UK
  • fYear
    2014
  • fDate
    10-12 Nov. 2014
  • Firstpage
    776
  • Lastpage
    783
  • Abstract
    Following the increasing influence of the formal study of argumentation in AI research, Abstract Argumentation (AA) is now taught as part of Computer Science degrees at various universities. To support the teaching of AA we present ARGTEACH, an interactive intelligent tutor that facilitates the learning of different labelling semantics in AA. The user assigns the labels in, out, and undec to arguments in an AA framework displayed as a graph, with the aim to find all complete labellings. The user then determines which of the complete labellings are grounded, preferred, semi-stable, or stable. During the labelling process, ARGTEACH supports the user by providing hints about possible next labelling steps, using a novel method for computing complete labellings, and by checking whether the labelling done so far is correct.
  • Keywords
    artificial intelligence; computer science education; educational institutions; graph theory; intelligent tutoring systems; teaching; AA; AI research; ARGTEACH; abstract argumentation; argumentation theory; computer science degrees; graph; interactive intelligent tutor; labelling semantics; learning tool; teaching; universities; Abstracts; Artificial intelligence; Educational institutions; Java; Labeling; Semantics; Computer Science Education; Electronic Learning; Logic;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on
  • Conference_Location
    Limassol
  • ISSN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2014.120
  • Filename
    6984556