This paper proposes a remote learning monitoring method based on learning behavior time series data to effectively monitor learning progress of students. This method integrates multi-scale feature extraction, a variational information bottleneck module, and a variational autoencoder to enhance feature diversity and clustering performance. Tests indicate that the proposed multi-scale full convolution algorithm model achieves a Precision of 0.887, an F1 score of 0.922, an area under the curve of 0.883, and a Recall of 0.960, outperforming benchmark algorithms such as Naive Bayes and chaotic lightning search algorithms in leak prediction. The improved unsupervised algorithm achieves a Precision of 0.888, a Recall of 0.944, an F1 score of 0.915, and an Accuracy of 0.861, surpassing benchmark algorithms. This study offers a high-precision solution for remote learning monitoring, which holds practical value in enhancing teaching quality, addressing learning challenges of students, and providing theoretical support for optimizing the learning environment. Future research will focus on further optimizing algorithm models.

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