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Authors: Aluizio Haendchen Filho 1 ; Fernando Concatto 1 ; Hércules Antonio do Prado 2 and Edilson Ferneda 2

Affiliations: 1 Laboratory of Technological Innovation in Education (LITE), University of Vale do Itajaí (UNIVALI), Itajaí, Brazil ; 2 Catholic University of Brasilia (UCB) QS 07, Lote 01, Taguatinga, Brasília, DF, Brazil

Keyword(s): Automated Essay Scoring, Machine Learning, Deep Learning.

Abstract: The National High School Exam (ENEM) in Brazil is a test applied annually to assess students before entering higher education. On average, over 7.5 million students participate in this test. In the same sense, large educational groups need to conduct tests for students preparing for ENEM. For correcting each essay, it is necessary at least two evaluators, which makes the process time consuming and very expensive. One alternative for substantially reducing the cost and speed up the correction of essays is to replace one human evaluator by an automated process. This paper presents a computational approach for essays correction able to replace one human evaluator. Techniques based on feature engineering and deep learning were compared, aiming to obtain the best accuracy among them. It was found that is possible to reach accuracy indexes close to 100% in the most frequent classes that comprise near 80% of the essays set.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Filho, A. H., Concatto, F., Antonio do Prado, H. and Ferneda, E. (2021). Comparing Feature Engineering and Deep Learning Methods for Automated Essay Scoring of Brazilian National High School Examination. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 575-583. DOI: 10.5220/0010377505750583

@conference{iceis21,
author={Aluizio Haendchen Filho and Fernando Concatto and Hércules {Antonio do Prado} and Edilson Ferneda},
title={Comparing Feature Engineering and Deep Learning Methods for Automated Essay Scoring of Brazilian National High School Examination},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={575-583},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010377505750583},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Comparing Feature Engineering and Deep Learning Methods for Automated Essay Scoring of Brazilian National High School Examination
SN - 978-989-758-509-8
IS - 2184-4992
AU - Filho, A.
AU - Concatto, F.
AU - Antonio do Prado, H.
AU - Ferneda, E.
PY - 2021
SP - 575
EP - 583
DO - 10.5220/0010377505750583
PB - SciTePress

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