Automatic Essay Scoring in a Brazilian Scenario
This addresses logistical and financial constraints in manual grading for Brazilian educational assessments, though it is incremental as it applies existing deep learning techniques to a new dataset.
The paper tackles the problem of automatic essay scoring for Portuguese-language essays in Brazil's ENEM exam, achieving results that align closely with human grading criteria to improve efficiency and scalability.
This paper presents a novel Automatic Essay Scoring (AES) algorithm tailored for the Portuguese-language essays of Brazil's Exame Nacional do Ensino Médio (ENEM), addressing the challenges in traditional human grading systems. Our approach leverages advanced deep learning techniques to align closely with human grading criteria, targeting efficiency and scalability in evaluating large volumes of student essays. This research not only responds to the logistical and financial constraints of manual grading in Brazilian educational assessments but also promises to enhance fairness and consistency in scoring, marking a significant step forward in the application of AES in large-scale academic settings.