Analyzing the Correlation Between Student Learning Behaviors and Psychological Atmosphere Using Deep Learning - Gu, Bo (International Journal of Interactive Mobile Technologies (iJIM), 2024)
With the rapid development of educational technology, the application of deep learning in analyzing student behavior and psychological states has become a hot topic in the field of education. This study, grounded in deep learning technology, aims to explore the correlation between student learning behaviors and psychological atmosphere, as well as the positive impact of a constructive psychological atmosphere on student learning. The background section discusses the limitations of traditional educational assessment methods and the necessity and urgency of applying deep learning in education. The current state of study section presents the progress in analyzing the correlation between student behavior and psychological states, highlighting the shortcomings in data processing and model construction in existing studies. Addressing these shortcomings, this study proposes a student learning behavior detection scheme based on the lightweight neural network shufflenetV2 and validates through empirical study the positive influence of a constructive psychological atmosphere on student learning behavior. The results show that utilizing a lightweight network model can effectively identify patterns in student learning behavior and, to some extent, predict the students’ psychological states. Furthermore, a positive psychological atmosphere indeed enhances students’ learning motivation and behavior. The methods and findings of this study hold significant theoretical and practical implications for advancing personalized and intelligent education.