A Data Mining-Based Method for Quality Assessment of Ideological and Political Education in Universities

Abstract

The procedure of assisting learning, gathering of information, skills, values, morals, beliefs, habits, and personal growth, is referred to as education. Education should focus not only on developing students’ professional abilities but also on developing and guiding students’ ideological and political perspectives. The purpose of the ideological and political education is to promote Marxist and sociology theories with Chinese features, as well as to bring students together for learning about the development of Marxist theory in China and the path to Chinese political identification. Students will be able to understand the superiority and scientific nature of socialism theory with Chinese characteristics along with actively preserving the socialism development path with Chinese characteristics. However, there are multiple weaknesses in the current evaluation of ideology and political science quality of teaching, including a misaligned evaluation goal, one object, and one-way evaluation. In order to solve these weaknesses, this study proposes an Apriori improvement approach for mining statistics from college political science classes. The important elements affecting instructors’ teaching quality are determined through a series of data collecting and data preprocessing procedures, which can assist teachers to improve and increase their teaching level as well as provide a strong foundation for teaching reform and management. During advanced stages, efficient monitoring and administration of teaching quality assessment can improve the scientific structure of teaching quality evaluation, give positive and constructive suggestions for political reform and development, and improve the college’s overall education level.