结合社会网络分析和多维特征聚类的学习小组划分方法

Grouping approach of learning team based on social network analysis and multidimensional feature clustering

通过引入社会网络分析方法,建立了综合学习能力、学习状态和协作能力多维指标的学习者特征模型,应用模糊相似和模糊等价演算,以多层次的模糊动态聚类方法识别候选学习领袖,通过融合多类型学习者实现学习小组的迭代划分。实验分析表明,该方法可提高虚拟学习社区中学习小组划分结果的准确度,具有良好的自适应性。

This paper presented a learner feature model considering learning capacity, learning status and cooperation ability by introducing social network analysis. Employing the fuzzy similarity and fuzzy equivalent operations, it put forward a group- ing approach of learning team, in which learning leader candidate were identified by using dynamic multi-level fuzzy clustering method, and iterative grouping could be achieved compromising learners of different levels. Finally, the experiment proves that this approach can enhance accuracy of learning team grouping with self-adaptability.