Research on Intelligent Student Behavior Monitoring and Analysis Model based on Deep Learning

With the advancement of education informatization, intelligent education system plays an increasingly important role in the analysis and monitoring of student behavior. Student behavior monitoring and analysis can not only provide personalized learning support, but also provide data-driven decision basis for teaching optimization. This paper proposes an intelligent student behavior monitoring and analysis model based on noise reduction autoencoder (DAE) and deep neural network (DNN), which aims to effectively extract features and reduce dimensions of student behavior data through autoencoder, and use deep neural network to learn and predict behavior patterns. First of all, this paper uses noise reduction autoencoder to carry out unsupervised learning of students' behavior data, achieve efficient compression and feature representation of multi-dimensional behavior data, reduce data redundancy, and extract key features of students' behavior. Then, a deep neural network (DNN) model is constructed to input low-dimensional features processed by autoencoders into the network to capture the dynamic behavior patterns of students during the learning process. DNN effectively improves the ability of fitting and predicting complex behavior patterns through the nonlinear mapping ability of multi-layer hidden nodes. In the experimental part, this paper verifies the student behavior data in the real education scene, and the results show that the proposed model is superior to the traditional methods in the accuracy of student behavior analysis. Through accurate behavior prediction, the model can provide educators with real-time feedback on student learning status and support the formulation of personalized teaching programs.