Ant Colony Algorithm to Explore the Optimal Path Planning Strategy of Curriculum Civics in Teaching English Reading Major

Abstract

This study introduces the ant colony algorithm in the field of education, aiming to optimize the integration path of curriculum Civics and English reading teaching. By constructing a path optimization model based on the ant colony algorithm, it simulates the dynamic interaction of teaching elements and realizes the autonomous optimization search of the integration path of curriculum Civics and politics. By combing the literature analysis method with the experimental research method, we systematically sorted out the papers related to curriculum ideology and politics in the core journals in the past five years, and refined the three key dimensions of value orientation, cultural infiltration, and emotional stimulation. Then experimental and control groups were set up in six different types of institutions to verify the effectiveness of the path through multi-dimensional data collection and analysis. The improved ant colony algorithm significantly enhances the coverage rate and depth of cultural penetration of the course's Civics elements by introducing a heuristic function that is sensitive to teaching characteristics. The results show that the path optimization strategy of curriculum Civics based on ACO algorithm not only improves students' English reading comprehension, but also significantly promotes the development of students' Civics literacy and comprehensive quality. The method provides quantifiable decision support for English reading teaching and promotes a paradigm shift from experience-driven to data-driven curriculum civics.