Optimised design of digital teaching resources for Civic and Political Education in colleges and universities under the integration of big data technology

It is impossible to separate the design and development of teaching resources from the teaching of ideological education in colleges and universities. However, the current digital teaching resources for ideological education exist in large quantities and vary in quality in many colleges and universities. In order to tackle these issues, the study employs big data technology for optimization. Specifically, it proposes an Apriori improvement algorithm, which is based on MapReduce parallelism, and an enhanced hybrid K-Means clustering algorithm, which is based on an intelligent single-particle algorithm. The optimized algorithms are then applied to the optimal design of digital teaching resources for civic and political education in a university. When the order of Civics courses is well switched, students have an 81.2% probability of getting “good” grades. The K-Means clustering algorithm divides 31 types of digital teaching resources for civics education in colleges and universities into 3 categories to enhance the quality of teaching.