A study on dynamic data modelling in the evaluation of the effectiveness of ideological education in colleges and universities based on big data

Civic and political education plays a key role in shaping the ideology and changing the behavioral activities of college students, and it has received widespread attention in the education sector. In this study, the entropy value method is used to analyze the weights of the constructed indicators for assessing the effect of civic and political education in colleges and universities and to design the assessment method for the effect of civic and political education in colleges and universities by combining the RBNN neural network model with the Adaboost algorithm. Subsequently, a dynamic assessment model of the effects of college civic education is constructed using the database of the academic affairs system and the assessment method. The results of the study show that the assessment model is feasible and reasonable, and the correct rate of assessment for the effect of college civic education is as high as 96.64%. After the educational intervention and strategy adjustment based on the assessment results of the dynamic assessment model, the assessment score of “teacher education” increased from 20.36 points (week 1) to 29.88 points (week 15). The dynamic assessment model proposed in this paper provides a greater impetus to improve the effectiveness of the school’s civic education, and it is hoped that it can provide practical and effective strategies for improving the political literacy of college students, enhancing their sense of identity and belonging to the country and the nation, and fostering the builders and successors of the socialist cause in the new era.