Real-Time Time Series-Based Behavior Anomaly Detection and Strategic Innovation in Edge Teaching Environments

With the development of educational informatization, multimedia teaching has become an essential means of higher education and has penetrated various disciplines. Traditional teaching methods have difficulty detecting and effectively intervening in students’ abnormal behavior in time, which affects students’ academic performance and future development. Recently, deep learning algorithms have been employed for abnormal behavior detection. However, existing studies focus on prediction accuracy and lack solutions and dynamic analysis. To further promote the development of students’ abnormal behavior detection technology and improve teaching quality, this paper proposes a time series-based framework for students’ abnormal behavior detection and learning behavior recommendation, which can dynamically identify and monitor students’ abnormal behavior. Specifically, LSTM and the transformer are used in this paper to capture the time dependence of students’ learning behaviors for binary classification prediction. Variational autoencoders (VAEs) are used as generative models to learn the distribution of learning behaviors of low-risk students and recommend learning behaviors for high-risk students. The experimental results show that the framework proposed in this paper can realize real-time monitoring and personalized intervention of students’ learning behavior, which is helpful for reforming teaching and improving teaching quality.