Park, Lee, & Shin (2021). 순환신경망 장단기 기억(LSTM)을 이용한 자동 채점의 가능성 탐색

[Dongkwang Shin]

박강윤, 이용상, 신동광. (2021). 순환신경망 장단기 기억(LSTM)을 이용한 자동 채점의 가능성 탐색. 교육과정평가연구, 24(4), 223-238.

박강윤 (한국지능정보사회진흥원)
이용상 (인하대학교)
신동광 (광주교육대학교)

Kangyun Park (Researcher, National Information Society Agency)

Yongsang Lee (Assistant Professor, Inha University)

Dongkwang Shin (Associate Professor, Gwangju National University of Education)

ABSTRACT

Exploring the Feasibility of an Automated Essay Scoring Model Based on LSTM

In the present study, the feasibility of an automated essay scoring of English was explored using Long-Short Term Memory (LSTM), a type of Recurrent Neural Network (RNN). LSTM is a deep learning model proposed to overcome the problem of long-term dependence of the existing RNN. In this study, an automated essay scoring model based on LSTM was adopted to score English essay data extracted from the open huge repository of data ‘kaggle,’ and the performance of the model was validated. Unlike multiple-choice scoring data which consisted of binary (true/false) data, essay scoring data had multiple facets, thus the data used for the deep learning model was constructed within a multinomial classification to order to predict scores of those essay data. For its validation, the six indices of ‘accuracy,’ ‘precision,’ ‘recall’, ‘F1-measure,’ ‘kappa,’ and ‘correlation coefficient’ were used. As a result, it turned out that the LSTM model could predict students' essay scores at an appropriate level. The performance of the deep learning model is closely related to the quality and quantity of data, thus it is expected that the accuracy of the automated essay scoring could be improved if sufficient quality data is composed and used for the deep learning process. To derive a more valid and reliable algorithm, it is necessary to conduct further empirical studies by testing various RNN models.

Key Words: RNN, Automated essay scoring model, LSTM

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