Self-organising communities formed by middle agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
The Stability, Scalability and Performance of Multi-agent Systems
BT Technology Journal
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IEEE Transactions on Knowledge and Data Engineering
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
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HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 4 - Volume 4
Effects of alternate representations of evidential relations on collaborative learning discourse
CSCL '99 Proceedings of the 1999 conference on Computer support for collaborative learning
FIE '98 Proceedings of the 28th Annual Frontiers in Education - Volume 03
Agent and multi-agent applications to support distributed communities of practice: a short review
Autonomous Agents and Multi-Agent Systems
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Web-based (or online) learning provides an unprecedented flexibility and convenience to both learners and instructors. However, large online classes relying on instructor-centered presentations could tend to isolate many learners. The size of these classes and the wide dispersion of the learners make it challenging for instructors to interact with individual learners or to facilitate learner collaborations. Since extensive literature has confirmed that the substantial impact of learner interaction on learning outcomes, it is pedagogically critical to help distributed learners engage in community-based collaborative learning and to help individual learners improve their self-regulation. The E-learning lab of Shanghai Jiaotong University created an artificial intelligence system to help guide learners with similar interests into reasonably sized learning communities. The system uses a multi-agent mechanism to organize and reorganize supportive communities based on learners' learning interests, experiences, and behaviors. Through effective award and exchange algorithms, learners with similar interests and experiences will form a community to support each others' learning. Simulated experimental results indicate that these algorithms can improve the speed and efficiency in identifying and grouping homogeneous learners. Here, we will describe this system in detail and present its mechanism for organizing learning communities. We will conduct human experimentations in the near future to further perfect the system.