Research on the Mechanism of Dynamic Monitoring of Undergraduate Students’ Learning Situation and Adaptive Adjustment of Teaching Mode--Based on the Perspective of Educational Evaluation Reform

Abstract

Abstract Dynamic monitoring of undergraduate learning is a tool for the accurate pulse of education and teaching, and an in-depth study of the quality monitoring data of students’ learning can discover the hidden problems and shortboards in education and teaching. The article utilizes information technology to build a dynamic monitoring platform for undergraduates. Then, it establishes an accurate teaching model for undergraduates with the assistance of this platform. The SHAP interpretable model is used to obtain the online learning behavior characteristics of undergraduates, and the LSTM autocoder is used to construct the time-varying feature sequence of undergraduates’ learning behavior, which is inputted into the LSTM model to establish the undergraduate learning situation early warning model. Taking the data of students’ online behavioral characteristics as an example, the validation of the importance of undergraduate learning behavioral characteristics and early warning is carried out, and the effect of the precise teaching model is also analyzed. The SHAP value of undergraduate students taking online tests is 0.969, which significantly impacts their online learning behavior. The accuracy of undergraduate students’ learning alert was 0.822, which was about 3.53% higher than the FWTS-CNN model with sub-optimal performance, and the results of the learning adaptability retest were 3.24 points higher than the initial test results. From the perspective of educational evaluation reform, combining undergraduate students’ learning dynamic monitoring data can enable adaptive adjustment of teaching content and mode.