Research on the Design of a System based on Machine Learning Algorithms for Automatic Scoring of English Writing Ability

Abstract

The development and implementation of an innovative system designed to automatically score English writing ability using advanced machine learning algorithms is challenging task. The core objective of the study is to establish a reliable and efficient method for assessing written English, which is crucial in educational and professional settings. The paper begins with an overview of the existing methods of English writing assessment, highlighting their limitations, such as time consumption and potential biases in human evaluation. The main focus of the study is the design and testing of a machine learning-based system. Various algorithms, including Natural Language Processing (NLP) techniques and neural network models, are explored and integrated to assess writing quality, grammar, coherence, and content relevance. The system’s architecture is detailed, explaining how these algorithms work in tandem to evaluate and score writing. An experimental setup is described where the system is trained and validated using a large dataset of English writing samples, ranging from beginner to advanced levels. The performance of the system is measured against traditional scoring methods, with emphasis on accuracy, consistency, and the ability to handle diverse writing styles and complexities. The results demonstrate the system’s proficiency in accurately scoring English writing, with a notable reduction in scoring time compared to human evaluators. The paper discusses the implications of these findings for educational institutions and language testing organizations, suggesting that this system could revolutionize how English writing is assessed.