Argumentative Learning with Intelligent Agents

Tao, Xuehong (2014) Argumentative Learning with Intelligent Agents. PhD thesis, Victoria University.


Argumentation plays an important role in information sharing, deep learning and knowledge construction. However, because of the high dependency on qualified arguing peers, argumentative learning has only had limited applications in school contexts to date. Intelligent agents have been proposed as virtual peers in recent research and they exhibit many benefits for learning. Argumentation support systems have also been developed to support learning through human-human argumentation. Unfortunately these systems cannot conduct automated argumentations with human learners due to the difficulties in modeling human cognition. A gap exists between the needs of virtual arguing peers and the lack of computing systems that are able to conduct human−computer argumentation. This research aimed to fill the gap by designing computing models for automated argumentation, develop a learning system with virtual peers that can argue automatically and study argumentative learning with virtual peers.

Item type Thesis (PhD thesis)
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1303 Specialist Studies in Education
Historical > Faculty/School/Research Centre/Department > College of Education
Keywords virtual arguing peers, learning systems, computer supported argumentation systems, computer models, chained knowledge, hierarchical knowledge, fuzzy dynamic knowledge, collaborative optimisation, automation, educational technologies, human-computer interaction, artificial intelligence, simulations, algorithms
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login