Author(s)

Junfang Xie

Corresponding Author

Junfang Xie

ABSTRACT

This paper discusses how to use artificial intelligence (AI) technology to optimize students' behavior analysis and teaching management strategies. Traditional teaching management methods rely on teachers' experience, and it is difficult to evaluate students' learning status comprehensively and objectively. In this study, through the construction of "Multidimensional Dynamic Learning Behavior Analysis Model (MD-LBAM)" and the application of machine learning, deep learning and natural language processing (NLP) technologies, the multi-dimensional data of students' learning duration, interaction frequency, homework completion and text feedback are deeply analyzed. MD-LBAM model can capture the temporal dynamics and potential patterns of students' learning behavior, accurately identify learning habits, preferences and potential problems, and generate a comprehensive analysis report on learning behavior. The experimental results show that the MD-LBAM model shows high accuracy in learning behavior feature extraction, such as learning habits (89.7%), learning preferences (84.3%), and the quality of homework completion (91.8%), which is highly consistent with the feedback from teachers and students. Based on this model, the personalized teaching scheme and the optimal allocation strategy of teaching resources have achieved remarkable results in improving students' learning effect and knowledge mastery. The experimental data show that the students in the experimental group are significantly better than those in the control group in terms of knowledge mastery and learning progress. This study not only enriches the theoretical basis of AI application in education, but also provides scientific basis and technical support for practical teaching management. Through the application of MD-LBAM model, personalized teaching, efficient use of resources and continuous optimization of teaching process can be realized, thus promoting the innovative development of education industry.

KEYWORDS

Artificial Intelligence; Student Behavior Analysis; Teaching Management