English writing quality evaluation method based on improved K-means clustering and machine learning

Abstract

The current English writing quality evaluation methods limit applicability and relatively low scoring accuracy. A quality evaluation model for English writing based on improved K-means clustering and machine learning is proposed to address the existing issues. The study first uses a continuous bag of words model to extract text vectors from the training set, and then applies an improved K-means clustering algorithm (KMCA) for text feature extraction. Afterward, the clustered text features are combined with deep learning features and scored using support vector machines to obtain annotated essay collections. The study applies this essay collection to the training of a writing quality evaluation model based on convolutional neural networks and long short-term memory networks (LSTM). The experiment outcomes indicate that the proposed English writing quality evaluation model has a fitting degree of over 0.9 with the evaluation results of professional teachers, and its recall rate and F1 score are 0.94 and 0.92, respectively. The accuracy of its evaluation results reaches 94.10%. Under different theme prompts, the Kappa coefficient value of the model reaches 0.81. The proposed writing quality evaluation model can achieve high-precision composition evaluation and provide reliable data feedback for students’ English learning.