Zhenyu Li
ABSTRACT
With the advancement of Internet technology, especially the development of 5G, massive open online courses(MOOCs) are more and more widely used. The exploration of issues related to MOOCs has become research hotspots in recent years. Through the analysis of the dataset of MOOCs, we extract the temporal characteristics of learning behavior of MOOCs users, calculate the similarity of the temporal characteristics, and perform cluster analysis and comparison on the temporal similarity of users to obtain user based imlicit communities divided by temporal characteristics. We apply K-means, SpectrClustering, and AgglomerativeCluster clustering algorithm to analysis the experimental data, and compare the experimental results on different numbers of clusters and different data size. We use silhouette coefficient to evaluate the effectiveness of clustering algorithm. The experimental results show that the analysis of the learning behavior of MOOCs can effectively realize the division of communities and contribute to the enhancement of MOOCs teaching.
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Digital Library
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Index Terms
- Research on the evaluation of learning behavior on MOOCs based on cluster analysis
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