Affiliations: [a] School of Computer Science, The Open University of China, Beijing, China | [b] Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing, China
Correspondence:
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Corresponding author: Yong Zhang, Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of artificial intelligence, Department of Information Science, Beijing University of Technology, Beijing, China. E-mail: [email protected].
Abstract: In the post-epidemic era, online learning has gained increasing attention due to the advancements in information and big data technology, leading to large-scale online course data with various student behaviors. Online data mining has become a popular and important way of extracting valuable insights from large amounts of data. However, previous online course analysis methods often focused on individual aspects of the data and neglected the correlation among the large-scale learning behavior data, which can lead to an incomplete understanding of the overall learning behavior and patterns within the online course. To solve the problems, this paper proposes an online course evaluation model based on a graph auto-encoder. In our method, the features of collected online course data are used to construct K-Nearest Neighbor(KNN) graphs to represent the association among the courses. Then the variational graph auto-encoder(VGAE) is introduced to learn the useful implicit features. Finally, we feed the learned implicit features into unsupervised and semi-supervised downstream tasks for online course evaluation, respectively. We conduct experiments on two datasets. In the clustering task, our method showed a more than tenfold increase in the Calinski-Harabasz index compared to unoptimized features, demonstrating significant structural distinction and group coherence. In the classification task, compared to traditional methods, our model exhibited an overall performance improvement of about 10%, indicating its effectiveness in handling complex network data.
Keywords: Educational data mining, online course evaluation, deep learning, graph auto-encoder
