In this study, Random Forest algorithm was applied using data of 402 Korean argumentative essays to develop an automatic scoring model for Korean essays. Recent studies on automatic scoring of essays are developing into studies using deep learning-based algorithms. However when automatic scoring is used in the field of education, deep learning-based algorithms have limitations because the interpretation and explanation of the scoring results must be possible. Therefore, in this study, we tried to develop a model that can interpret the scoring results by applying a machine learning-based algorithm that utilizes scoring features. In the essay, various parts of morpheme-based scoring qualities were derived, and the number of sentences was used. As a result, it was possible to derive a significant level of model performance. However, this study has a limitation in that it utilized basic quantitative feature such as parts of morpheme frequency and number of sentences. In future research, more in-depth grading feature will be developed to explore ways to provide more meaningful educational feedback through automated essay scoring.
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