An English Educational ability Evaluation Model Considering Big Data Improved K-means Clustering Algorithm

As teaching methods and content develop variously, assessing English educational ability has become a crucial component of English education and a key research focus. The traditional English educational ability evaluation model has problems such as low accuracy and efficiency. To address these issues, the study proposes an English educational ability evaluation model based on a big data-improved K-means Clustering Algorithm (KCA). The study first uses big data indicator coefficients to perform cluster analysis on educational ability data. Then, based on clustering analysis, the KCA clustering algorithm is improved using the Least Squares (LS) method, and the improved method is used to construct an English educational ability evaluation model. The results indicated that the weight values and data cleaning errors of this model in the educational ability assessment were 0.29 and 5.13 %, respectively. Concurrently, enhancements were made to the KCA method, noise reduction by 36.19% and fitting distribution error by 3.59%. The average amplitude and distribution coordination of its waveform fluctuations were 1.63 % and 91.26 %, respectively. Its test results were superior to the comparison methods. This indicates that the constructed model has better clustering performance and higher classification accuracy in English educational ability evaluation. This helps to provide more objective and scientific evaluation methods for English education. The research aims to provide more reliable data to support the development of English education.