A Collaborative Study of English Teaching Based on Optimized Apriori Algorithm under the Integration of Curriculum Civics

Abstract

The new period introduces new intellectual and political demands for college-level English courses and fosters favourable conditions as well as new intellectual and political opportunities. The knowledge and politics courses currently taught in our higher education institutions combine English instruction with cooperative research and knowledge and politics courses, producing some excellent results in China. However, there are still significant challenges to implementing the relatively new ideas for education reform. In order to increase the effectiveness of the mine, this paper will enhance the apriori calculation method in accordance with the features of a large amount of data, and it requests the English Education Graduate School's participation for a demonstration. To address the issue of inaccurate existing metrics, this document integrates the proposed measures with conventional measures. In the past, the new measuring technique and the enhanced algorithm were used to create a model of the mining system based on the performance of the pupils. In the end, we used the student successes to build the mine system model, integrate the new measurement technique with the improved algorithm, and draw significant data-driven conclusions. According on experimental findings, the proposed algorithm performs better than Mr. Apriori. The mixed metre tonnes serve as the foundation for the most precise selection as compared to the general metre tonnes. The system offers a range of data extraction services in accordance with the university's role, which serves to enhance college English education's collaboration with curriculum and governmental thinking.