CLC number: TP391.7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
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YANG Fan, SHEN Rui-min, HAN Peng. A novel self-organizing E-Learner community model with award and exchange mechanisms[J]. Journal of Zhejiang University Science A, 2004, 5(11): 1343-1351.
@article{title="A novel self-organizing E-Learner community model with award and exchange mechanisms",
author="YANG Fan, SHEN Rui-min, HAN Peng",
journal="Journal of Zhejiang University Science A",
volume="5",
number="11",
pages="1343-1351",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.1343"
}
%0 Journal Article
%T A novel self-organizing E-Learner community model with award and exchange mechanisms
%A YANG Fan
%A SHEN Rui-min
%A HAN Peng
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 11
%P 1343-1351
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.1343
TY - JOUR
T1 - A novel self-organizing E-Learner community model with award and exchange mechanisms
A1 - YANG Fan
A1 - SHEN Rui-min
A1 - HAN Peng
J0 - Journal of Zhejiang University Science A
VL - 5
IS - 11
SP - 1343
EP - 1351
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.1343
Abstract: How to share experience and resources among learners is becoming one of the hottest topics in the field of E-Learning collaborative techniques. An intuitive way to achieve this objective is to group learners which can help each other into the same community and help them learn collaboratively. In this paper, we proposed a novel community self-organization model based on multi-agent mechanism, which can automatically group learners with similar preferences and capabilities. In particular, we proposed award and exchange schemas with evaluation and preference track records to raise the performance of this algorithm. The description of learner capability, the matchmaking process, the definition of evaluation and preference track records, the rules of award and exchange schemas and the self-organization algorithm are all discussed in this paper. Meanwhile, a prototype has been built to verify the validity and efficiency of the algorithm. Experiments based on real learner data showed that this mechanism can organize learner communities properly and efficiently; and that it has sustainable improved efficiency and scalability.
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