A Study on the Impact of Age Factors on Knowledge Sharing and Management in Big Data-Driven English Teaching

Abstract

Age is a crucial factor that influences individual differences in second language acquisition. The development of cognitive abilities also plays a significant role in second language learning, with the effects varying depending on whether this influence is positive or negative. This study explored the impact of age on knowledge sharing and management in big-data-driven English language teaching, aiming to optimize the teaching model of English wisdom classes. The paper proposes a detailed design tailored to different age groups, integrating relevant theories of intelligent classrooms and their teaching models. Additionally, the study parallelizes the improved k-nearest-neighbor algorithm to better suit the data processing requirements of cloud computing platforms. Upon parallelization, the enhanced k-nearest-neighbor algorithm is implemented on the Spark cloud platform, resulting in an approximately 10.3% increase in algorithm efficiency while maintaining classification accuracy.