Title :
Arg Teach - A Learning Tool for Argumentation Theory
Author :
Dauphin, Jeremie ; Schulz, Claudia
Author_Institution :
Dept. of Comput., Imperial Coll. London, London, UK
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;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on
Conference_Location :
Limassol
DOI :
10.1109/ICTAI.2014.120