E-ISSN: 6575-5565
P-ISSN: 3427-2556
DOI: https://iigdpublishers.com/article/1050
Intelligent systems of essay grading constitute important tools for educational technologies. They can significantly replace the manual scoring efforts and provide instructional feedback as well. These systems typically include two main parts: a feature extractor and an automatic grading model. The latter is generally based on computational and artificially intelligent methods. In this work, we focus on the feature extraction part. More precisely, we focus on argumentation and discourserelated features, which constitute high-level features. We discuss some state-of-theart systems and analyze how argumentation and discourse analysis are used for extracting features and providing feedback.
Naima DEBBAR
Azmi, A. M., Al-Jouie, M. F., & Hussain, M. (2019). AAEE–Automated evaluation of students’ essays in Arabic language. Information Processing and Management, 56(5), 1736-1752.
Burstein, J. (2003a). The E-rater® scoring engine: Automated essay scoring with natural language processing.
Burstein, J., Marcu, D., & Knight, K. (2003b). Finding the WRITE stuff: Automatic identification of discourse structure in student essays. IEEE Intelligent Systems, 18(1), 32-39.
Cropley, D. H., & Marrone, R. L. (2022). Automated scoring of figural creativity using a convolutional neural network. Psychology of Aesthetics, Creativity, and the Arts.
Dasgupta, T., Naskar, A., Dey, L., & Saha, R. (2018, July). Augmenting textual qualitative features in deep convolution recurrent neural network for automatic essay scoring. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications (pp. 93-102).