Optimization of University English Blended Teaching Course Design Based on Association Rule Mining

Abstract

In the ever-evolving landscape of education, the integration of technology has become paramount in shaping effective teaching methodologies. Blended learning, combining traditional classroom instruction with online elements, offers a promising approach to cater to diverse learning styles and enhance student engagement. This paper presents a novel methodology for optimizing university English blended teaching course design through association rule mining. Leveraging data mining techniques, particularly association rule mining, we analyze the intricate relationships among various components of the course structure, including teaching methods, instructional materials, assessment techniques, and student engagement strategies. Through the discovery of meaningful associations, represented as "if-then" rules, we uncover insights into the factors that significantly impact the effectiveness of the blended learning experience. By iteratively refining the course design based on these insights and validating the optimized approach through empirical evaluation, we aim to enhance the quality of English language education at the university level. The proposed methodology not only contributes to the advancement of educational research but also provides practical implications for educators and curriculum developers seeking to harness the potential of blended learning to foster student learning outcomes and academic success.