Yong-Jin Kim

You-Jin Jong, Yong-Jin Kim, Ok-Chol Ri

Automated essay scoring plays an important role in judging students' language abilities in education. Traditional approaches use handcrafted features to score and are time-consuming and complicated. Recently, neural network approaches have improved performance without any feature engineering. Unlike other natural language processing tasks, only a small number of datasets are publicly available for automated essay scoring, and the size of the dataset is not sufficiently large. Considering that the performance of a neural network is closely related to the size of the dataset, the lack of data limits the performance improvement of the automated essay scoring model. In this paper, we proposed a method to increase the number of essay-score pairs using back-translation and score adjustment and applied it to the Automated Student Assessment Prize dataset for augmentation. We evaluated the effectiveness of the augmented data using models from prior work. In addition, performance was evaluated in a model using long short-term memory, which is widely used for automated essay scoring. The performance of the models was improved by using augmented data to train the models.

You-Jin Jong, Yong-Jin Kim, Ok-Chol Ri

The first automated essay scoring system was developed 50 years ago. Automated essay scoring systems are developing into systems with richer functions than the previous simple scoring systems. Its purpose is not only to score essays but also as a learning tool to improve the writing skill of users. Feedback is the most important aspect of making an automated essay scoring system useful in real life. The importance of feedback was already emphasized in the first AES system. This paper reviews research on feedback including different feedback types and essay traits on automated essay scoring. We also reviewed the latest case studies of the automated essay scoring system that provides feedback.

Yong-Jin Kim, Yong-Min Kim, Yong-Jin Choe et al.

Recent study on certificateless authenticated key agreement focuses on bilinear pairing-free certificateless authenticated key agreement protocol. Yet it has got limitations in the aspect of computational amount. So it is important to reduce the number of the scalar multiplication over elliptic curve group in bilinear pairing-free protocols. This paper proposed a new bilinear pairing-free certificateless two-party authenticated key agreement protocol, providing more efficiency among related work and proof under the random oracle model.