Exploring the Feasibility of an Automated Essay Scoring Model Based on LSTM
Kangyun Park, Yongsang Lee, Dongkwang Shin · Journal of Curriculum and Evaluation · 2021
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