Mathematical Analysis and Assessment of the Effectiveness of Personalized Implementation of Ideological and Political Education in Colleges and Universities in a Big Data Environment

Abstract

The personalized teaching mode is based on individualized needs, and through the support of big data analysis technology, it provides students with more accurate and suitable educational resources to maximize the learning effect. In this paper, we will explore the personalized teaching mode in the field of ideological and political education from the perspective of big data analysis technology, explore the main factors affecting the personalized teaching effect of ideological and political education in colleges and universities by factor analysis method, and construct an evaluation model of the personalized implementation effect of ideological and political education. Using factor analysis and principal component method, the study identifies three common factors affecting the personalized implementation effect of ideological and political education in colleges and universities from 15 indicators, which are: technology integration and platform support efficacy, pedagogical adaptation and personalized experience value, and personalized education goal consensus. Afterwards, a factor score model for assessing the implementation effect of personalized ideological and political education in colleges and universities was constructed based on the regression method: F=(F1∗48.446+F2∗29.985+F3∗16.908)/95.339. The evaluation model reduces the cross and redundancy of information, and the evaluation results are scientific and reliable with strong operability and certain value of popularization and application.