English Language Proficiency Analysis and Prediction using Deep Learning Algorithms

Abstract

Abstract: Writing is one of the fundamental abilities. Unfortunately, only a small part of children are able to obtain it, typically as a result of the infrequent use of writing projects in the classroom. The lack of exercise has a particular negative effect on students who are studying English as a second language, also known as English Language Learners (ELLs), a population of students that is constantly growing. ELLs are not the intended audience for these tools, despite the fact that employing automated feedback systems makes it easier for teachers to offer more writing assignments. Due to the present tools' incapacity to provide feedback based on the student's language proficiency, the learner may receive a final evaluation that is skewed in their favor. Using data science models and algorithms, automated feedback techniques may be enhanced to better address the unique needs of these children. The primary goal of this essay is to assess the quality of the essay using a set of evaluation criteria. Six analytical criteria were used to evaluate the essays: coherence, syntax, vocabulary, phraseology, grammar, and conventions. Each measure's scores grow by a factor of 0.5 and range from 0.0 to 5.0. Predicting how well an article will perform on these 5 metrics is the major task