Exploration and Practice of English Civics Teaching Design in Colleges and Universities Based on Big Data Analysis

Abstract

This study explored the cutting-edge methodology of integrating curricular Civics into teaching specialized disciplines in higher education, an essential trend for the future development of higher education. By applying data-mining algorithms, the article constructs a big-data analysis framework from the dimensions of students’ classroom behavior and performance. The fuzzy C-mean clustering algorithm was optimized using the cuckoo algorithm to effectively classify the characteristics of students’ behavior in the Civics classroom, and the attention-based hybrid encoder-decoder model was used to accurately predict students’ grades. After establishing a complete analytical framework, an innovative college English Civics teaching model was developed following the ASSURE teaching model. It was applied and analyzed for its effect in actual teaching. The study results show that, after the implementation of the English Civics and Politics course based on the ASSURE model, the average grade of the students increased by about 0.5 points. The grades were distributed between 80 and 95 points, significantly different from the pre-test grades (P<0.05). The prediction error was controlled to be less than ±2 points, which indicates that the teaching model had a significant teaching effect. This study not only improves English learning performance but also promotes the improvement of students’ performance in Civics, showing the great potential and value of integrating Civics curriculum into professional teaching.

Keywords

• cuckoo algorithm
• fuzzy c-mean clustering
• hybrid encoder
• assure teaching
• english civics teaching
• 94a08