Automatic Scoring of English Essays Based on Machine Learning Technology in a Wireless Network Environment

Abstract

The English composition is an important indicator of English learners’ overall language skills and is asked in large-scale English examinations, both in China’s college entrance examinations and graduate examinations and in the TOEFL, GRE, and IELTS examinations in Europe and the United States. Some automatic scoring systems for English writing have been created in the United States and internationally, however the systems still have issues with generalization, accuracy, and error correction. In this paper, we present a method to improve the accuracy of existing automatic composition scoring systems through deep learning techniques in a wireless network environment. Experiments reveal that the method can accurately assess the quality of English learners’ writings, paving the way for the creation of an automated composition scoring system for large-scale machine testing and web-based self-learning platforms.

An Automated English Essay Scoring Engine Based on Neutrosophic Ontology for Electronic Education Systems

“…The model is split into two parts: a sequence-generating network that can forecast the probabilities of individual words and an assessment network that may predict the final results of sentences. In order to increase the reliability of automatic writing scoring systems, the authors in [41] devised a methodology to use deep learning methods in a wireless network environment. The results of the experiments show that the technique is effective in evaluating the writing skills of English as a second language (ESL) students, opening the door to the development of an automatic composition grading system for use in massive online machine exams and self-study websites.…”

Retracted: Automatic Scoring of English Essays Based on Machine Learning Technology in a Wireless Network Environment

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”