Course Design Based on Association Rule Mining in Blended Teaching of College English

Abstract

Course design based on association rule mining in blended teaching of college English offers a data-driven approach to curriculum development, enhancing the effectiveness of English language instruction. By analyzing patterns and relationships within student performance data, association rule mining algorithms identify correlations between different learning activities, topics, and assessment outcomes. This information enables instructors to design courses that strategically integrate online and offline learning components, ensuring a cohesive and engaging learning experience. Additionally, association rule mining facilitates the identification of potential dependencies between course elements, allowing for the optimization of teaching sequences and resource allocation. This paper investigates the use of the Extraction Apriori Rule Mining Blended (EARMB) technique to enhance teaching effectiveness in English instruction within blended learning environments. Through the analysis of association rules derived from instructional data, significant relationships between instructional components and student outcomes are identified, providing valuable insights for course design and instructional strategy development. A structured course design framework is presented, integrating various instructional activities and assessment methods to address diverse language skills and learning objectives. Additionally, a comparative analysis of different teaching approaches, including traditional classroom instruction, online learning platforms, and blended teaching, demonstrates the efficacy of blended teaching in promoting student engagement, improving learning outcomes, and enhancing overall student satisfaction.