Personalized Recommendation of Teacher Learning Behavior Using Expression Recognition Algorithms Integrated by Deep Learning and Neural Networks

At present, online teacher training platforms represented by Chinese university catechism have become an important way for teachers' professional development. However, the course dropout rate of such platforms is very high. For this reason, this paper intends to utilize a combination of deep learning and augmented learning to monitor the teacher's facial expression in the process of online teaching in real time, so as to realize the analysis of the teacher's learning behavior and emotional state. This paper proposes to investigate a facial expression recognition method based on a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), using augmented learning strategies to train and optimize the proposed model to better understand the teacher's emotional response and learning needs. In this paper, we use machine learning and other methods to study teachers' emotional changes in the specific teaching process and conduct correlation analysis with them to find out the important factors affecting their learning effectiveness. When the teacher's emotion reaches a pleasant state, the learning completion rate is significantly increased by 20%, indicating that the emotional state has a significant positive impact on learning behavior and performance. Through the in-depth analysis of teachers' facial expressions and learning behaviors, we can optimize the appropriateness and engagement of the training content, as well as provide new research ideas and methods for educational technology research and promote the development of educational informatization.