An Online Learner Clustering Algorithm Incorporating Autoencoder And Canopy-Kmeans

As online courses are more and more widely used in college courses, the analysis of online learning data has become an important means to improve teaching quality and learning effect. In this paper, an online learner clustering algorithm that integrates Autoencoder and Canopy-Kmeans is proposed. The Autoencoder is used to perform feature degradation and feature extraction for high-dimensional data which effectively solves the problems of data sparsity and dimensionality disaster. The Canopy algorithm is introduced to precluster the data which reduces the randomness of the K-means algorithm during initialization and improves the stability of clustering. And the improved K-means algorithm is used to perform the clustering analysis of text data. The experimental results show that the algorithm proposed behaved better in both clustering accuracy and noise resistance compared with traditional clustering algorithm, which is helpful to a better understanding of the behavioral characteristics of online learners and a better support for personalized teaching is provided based on this.