Research on English Online Teaching Model Based on Association Rules Driven by Big Data

In today’s information age, big data has become an indispensable and important resource in various fields, and the education sector is no exception. With the explosive growth of educational data, how to effectively mine and utilize this data to optimize the education and teaching process has become a focus of attention for educators and researchers. Among them, association rule mining, as an important data mining technique, is increasingly widely used in the field of education. This investigation delves into the deployment of association rule mining within the framework of English online teaching models, capitalizing on the burgeoning domain of big data. In the era of exponentially advancing information technology, big data has crystallized as an integral component for educational enhancement in both pedagogical quality and methodologies. Initially, this paper dissects a spectrum of extant teaching paradigms propelled by big data analytics. The discourse then pivots to scrutinize the contemporary landscape and the evolution of English online pedagogy. Employing association rule analysis, the study excavates a trove of significant patterns and linkages from voluminous datasets of online educational activities. The insights gleaned serve as a compass for refining instructional strategies and judiciously distributing educational resources. The empirical evidence underscores the proposition that granular examination of student engagement metrics and scholastic achievement empowers educators to tailor bespoke educational trajectories, thereby amplifying pedagogical efficacy and enriching the academic voyage. Beyond furnishing an avant-garde outlook on English online instruction, the findings proffer a substantive benchmark for e-pedagogy across diverse academic disciplines.