Intelligent agent-assisted decision support for personalised virtual learning environment
Shijia Gao, Dongming Xu · 2007
In the delivery of online learning, Virtual Learning Environments (VLEs) have been growing quickly and research on intelligent agent supported eLearning systems has developed rapidly in the last decade. However, some online learning programs have not been successful, and one of the main reasons could be that those online learning programs supported by VLEs have not fully considered learners' differences. VLEs developed under constructivism and embedded personalization learning functions have a potential to meet different requirements of different learners. In order to provide decision support for personalization decisions in VLEs, we formulate a conceptual model for personalization by following Simon’s decision-making process model. Based on this model, in order for a more adaptive, intelligent and flexible solution for personalized virtual learning environment (PVLE), the intelligent agent technology is applied in this research. Intelligent agent technologies with features such as autonomy, pre-activity, pro-activity and co-operativity, facilitate the interaction between students and the systems. By applying intelligent agents in PVLEs, individual learners can be uniquely identified, with content specifically presented for them, and progress can be individually monitored, supported, and assessed. Several types of agents are proposed and a novel and open multi-agent architecture is presented for PVLE. A prototype system for PVLE is also developed to demonstrate the advances of the proposed system architecture.