Research on Real-time Learning State Analysis and Feedback Model for Mobile Learning in the Course of “Fresh E-commerce and Cold Chain Logistics”

With the rapid development of education informatization, mobile learning has become an important development direction for higher education due to its flexibility and personalization advantages. However, there are still some problems in the current mobile learning system, especially in the teaching of practical courses, such as the inability to dynamically monitor the learning status of students, and the lack of real-time feedback mechanism and effective personalized learning support. This study focuses on these problems and proposes a real-time learning status analysis and feedback model based on Chaoxing Learning Platform. Through data collection, K-means clustering analysis, decision tree classification and ARIMA time series analysis, the model can accurately analyze students' learning behavior and generate dynamic feedback. In the experimental part, we use randomized grouping to compare the differences in learning efficiency, test scores and learning satisfaction between the experimental group using the feedback model and the control group using traditional learning methods. The results show that the experimental group's learning efficiency increased by an average of 10-15%, test scores increased by an average of 8 points, and learning satisfaction was also about 15% higher than that of the control group. In addition, cluster analysis revealed the grouping differences in students' learning status, and time series analysis depicted the dynamic trend of learning efficiency, which further validated the effectiveness of the feedback model. The innovation of this study is that it proposes a data-driven dynamic feedback mechanism, which not only enhances the students' learning experience, but also provides new ideas for the optimization of practical course teaching. It fills the gap of real-time feedback research in mobile learning environment, which has important reference value at both theoretical and practical levels, and also provides reliable technical support for the improvement of personalized teaching mode.