Automatic Essay Scoring in a Brazilian Scenario
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914625830780928 |
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| author | Matsuoka, Felipe Akio |
| author_facet | Matsuoka, Felipe Akio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00095 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Automatic Essay Scoring in a Brazilian Scenario Matsuoka, Felipe Akio Computation and Language 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. |
| title | Automatic Essay Scoring in a Brazilian Scenario |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.00095 |