Abstract: This paper proposes an online learning hybrid grouping algorithm based on fuzzy C-means algorithm  according to learners model with multidimensional features. Firstly, learners multidimensional features components are extracted. Then, based on the homogeneity clustering theory, the fuzzy C-means algorithm is used to cluster the learners according to their learning styles, knowledge levels, learning objectives and interests. At last, based on the heterogeneity clustering theory, learners are grouped according to their activeness and gender. The proposed algorithm not only ensures that learners with similar learning styles, knowledge levels, learning objectives and interests are grouped into the same ones but also takes into account the effects of activeness and gender differences in learning, which makes group division is more reasonable. Experiments show that the proposed algorithm is superior to the traditional grouping methods, the learner’s learning effectiveness and learning satisfaction have been greatly improved.

Key words: online collaborative learning, learners&rsquo, characteristics, group division, Fuzzy C-Means algorithm（FCM）

摘要： 在构建了学习者多维特征模型的基础上，设计了基于模糊C均值的在线协作学习混合分组算法。提取学习者多维特征分量，通过模糊C均值算法以学习风格、知识水平、学习目标和兴趣爱好为主要特征进行同质聚类，根据活跃度和性别特征进行异质聚类以实现混合性质分组。该算法将异质和同质分组相结合，既保证了学习风格、知识水平、学习目标和兴趣爱好具有相似性的学习者划分到同一组，同时考虑到了活跃度和性别差异对学习效果的影响，使得小组划分更加合理。实验表明，该算法优于传统分组方法，学习者的学习效果和学习满意度都有较大提升。

关键词: 在线协作学习, 学习者特征, 小组划分, 模糊C均值