基于双路聚类的在线学习行为分析研究

为有效地利用日志文件，更有深度地刻画学习者画像，提出了双路聚类建模方法(Two-way Clustering, TWC)，分析挖掘了万余人次学习者在某大学网络教育学院的大量学习行为数据，力图更深刻地展现远程教育学习者的风貌. 考虑到教育数据具有隐含性这一特点，该方法以细粒度数据为核心，通过双角度的聚类计算得到了各学习者在不同模型中的类别，最后基于融合后的模型对学习者进行刻画. 4种经典聚类算法与TWC算法的对比实验结果和TWC算法的聚类结果表明:TWC算法能够增强簇的内聚性，更准确地对学习者进行聚类，从而更深刻、更全面地刻画学习者轮廓.

An Analysis of Online Learning Behavior Based on Two-way Clustering

In order to make effective use of log files and depict learners' portraits more deeply, a two-way clustering modeling method(TWC) is proposed and used to analyze and mine the data of learning behavior of more than ten thousand learners in the College of Network Education of a university. Considering the implicit characteristic of educational data, this method takes fine-grained data as the core, obtains the categories of learners in different models through two-way clustering calculation, and finally describes learners based on the fused model. The experimental results of four classical clustering algorithms and the TWC algorithm and the clustering results of the TWC algorithm show that the TWC algorithm can enhance the cohesion of clusters and cluster learners more accurately and describe the learners' profiles more deeply and comprehensively.