Using Cutting-Edge Natural Language Processing Techniques to Enhance Essay Grading Algorithms
Gaurav Kumar, N. Hema, Vijay Kumar, K S Punithaasree, S Prema, M Vimochana. · 2024
Essay grading is one of the most cumbersome tasks that, so far, have depended on human scores to evaluate linguistic proficiency, coherence, and argumentation. Yet this work aims to present an alternative way of addressing the automation of essay grading based on advanced techniques of NLP. In more detail, it utilizes a hybrid deep learning model that combines BERT with LSTM networks to capture semantic understanding and sequential context in student essays. Use BERT to extract rich contextual embeddings allowing for deeper text understanding, then further process them with LSTM for retaining long dependencies while offering strong predictions. It learns subtle nuances in how essays should be graded along dimensions such as grammar, coherence, or argumentation structure by learning from a dataset of student essays graded by human experts. This approach indicates significant improvement in grading accuracy beyond traditional machine learning models and offers more reliable and scalable solutions for automated essay scoring. Thus, BERT-LSTM displays high performance in the rating task of essays with minimal or no human interference in the case of the minority class as it reduces bias and maintains consistency in diverse topics and writing styles. The research contribution will be onto this newly emerging field of educational technology, providing an innovative tool that enhances the efficiency of grading an essay fairly and, therefore, supports educators and improves learning outcomes for students.