An English Education Ability Evaluation Model Considering Big Data Improved K-means Clustering Algorithm

As teaching methods and content develop variously, assessing English education ability has become an important support in English education and a research hotspot. The traditional English education ability evaluation model has problems such as low accuracy and efficiency. To address these issues, the study proposes an English education ability evaluation model based on a big data-improved K-means clustering algorithm. The study first uses big data indicator coefficients to perform cluster analysis on educational ability data. Then, based on clustering analysis, the K-means clustering algorithm is improved using the least squares method, and the improved method is used to construct an English education 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 K-means method, resulting in a reduction in noise and a more optimal distribution, with a 36.19% and 3.59% reduction in fitting errors, respectively. 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 education 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.