基于方向权值标签传播的微学习单元聚类算法

为帮助学习者从大量在线学资源中找到适合自身个性化的学习资源及顺序集合,提出一种基于有向边方向权值的标签传播算法(LPADEW)用于发现适合特定学习者并属于同一学习周期的微学习单元序列簇群。该算法对标签传播算法进行两个改进：根据单元节点的利用度确定标签的更新顺序,降低在节点更新顺序上的随机性；利用当前单元节点的前置邻居和后置邻居的有向边权累加值进行标签更新,并将标签权重引入标签更新策略,既可降低标签更新的随机性,也可避免形成巨型簇群。实验结果表明,LPADEW算法在微学习真实数据集和人工数据集中均取得了较好的结果。

Micro-Learning Units Clustering Algorithm Based on Directed Edge Weights Label Propagation

In order to help learners identify suitable learning resources and sequences sets for personalized learning from a large number of online learning resources, a label propagation algorithm on directed edge weights (LPADEW) is proposed for clustering micro-learning units and sequence that belong to the same learning period for specific learners. The algorithm makes two improvements to the label propagation algorithm: the label update order is determined according to the utilization rate of the unit node, the randomness of node update order is reduced; the accumulated value of the directed edge weights of the pre-neighbor and the post-neighbor of the current element node is used to update the label, and the label weight is used in the label updating strategy, which can not only reduce the randomness of label updating, but also avoids the formation of giant cluster. Experimental result shows that LPADEW algorithm performs well on real datasets of micro-learning and synthetic datasets.