The Optimization of an English Writing Automated Assessment Model Based on Big Data Analysis

Abstract

This study proposes an optimized automated English writing assessment model based on big data analysis techniques. The model aims to address the limitations of traditional manual grading and existing automated systems by leveraging multi-source data to enhance the assessment's comprehensiveness and accuracy. By utilizing deep learning algorithms, the model can assess multiple dimensions of writing, including grammar, content, structure, and style. Furthermore, it incorporates dynamic adaptation strategies to provide personalized feedback tailored to individual learner needs, thereby supporting continuous learning progression. Empirical validation of the optimized model demonstrates significant improvements in error correction and content evaluation accuracy across different proficiency levels. This research offers a potential solution to large-scale, efficient, and objective English writing evaluation and provides insights into the future direction of integrating big data and AI into educational technologies.