Storage and Allocation of English Teaching Resources Based on k-Nearest Neighbor Algorithm


  • Yi Lou Zhengzhou Preschool Education College



teaching resources, k-nearest neighbor (kNN) algorithm, erm frequency-inverse document frequency (TF-IDF) weight, storage and allocation


The boom of Internet technology gives a boost to the informatization of education in China. Internet resources serve as a new carrier of knowledge, offering teachers and students an alternative to books. However, the exponential growth of Internet resources has greatly complicated the storage and allocation of resources. This paper attempts to fully utilize English teaching resources through effective resource management and allocation. Specifically, the features of English teaching resources were analyzed, and then the term frequency-inverse document frequency (TF-IDF) weight method and k-nearest neighbor (kNN) algorithm were improved to make resource allocation more efficient and effective. The improved methods were then verified through a case analysis. The results show that the improved kNN provides a feasible way to allocate English teaching resources. The research findings provide reference to the storage and allocation of teaching resources.

Author Biography

Yi Lou, Zhengzhou Preschool Education College

Yi Lou, female, was born on August 23,1967, majored in English language and literature. She is currently working as an English teacher in Zhengzhou Pre-school Education College, Henna, China. She has been teaching English courses for 31 years, including College English course and English Education course. Currently, she is serving as the Director of English Teaching and Research. She has published five books and several papers on English teaching. In 2013, she is excellent in annual assessment. In 2016, she was rated as an expert of professional education in Henan Province.




How to Cite

Lou, Y. (2019). Storage and Allocation of English Teaching Resources Based on k-Nearest Neighbor Algorithm. International Journal of Emerging Technologies in Learning (iJET), 14(17), pp. 102–113.