An Improvement of Matrix-based Clustering Method for Grouping Learners in E-Learning

Clustering learners into groups according to the customized features in e-learning environment is an important step to build a personalization learning system. Though clustering e-learners is important for better cooperation between teachers and students in e-learning, it is a challenge job to group learners flexibly and exactly. Since there are already many models for the features which are used for the basis of the clustering methods, this paper proposes an improvement of Matrix-based Clustering Method which preformed efficiently without extra comparison in contrast to k-means clustering algorithm. The improvement of the Matrix-based Clustering Method proposes the concept "Agglomerate Strength" for further cluster cohesion measurement in contrast to the previous Matrix-based Clustering Method in precision. And the comparison experiments between the improvement Matrix-based Clustering Method and the other methods, i.e. the previous Matrix-based Clustering Method and K-means algorithm, are investigated. The results of experiments show that this method is feasible and efficient.

在网络学习环境中，根据定制化特征将学习者聚类成组是构建个性化学习系统的重要步骤。尽管对网络学习者进行聚类对于网络学习中师生更好地合作很重要，但灵活且准确地对学习者进行分组是一项具有挑战性的工作。由于已经有许多用于聚类方法基础的特征模型，本文提出了一种基于矩阵的聚类方法的改进，与k - 均值聚类算法相比，该方法无需额外比较即可高效执行。基于矩阵的聚类方法的改进提出了“凝聚强度”的概念，以便在精度上与之前的基于矩阵的聚类方法相比，进一步测量聚类的内聚性。并且对改进的基于矩阵的聚类方法与其他方法（即之前的基于矩阵的聚类方法和K - 均值算法）进行了对比实验研究。实验结果表明该方法是可行且高效的。