Bibliography

Amorim, E., Cançado, M., & Veloso, A. (2018). Automated Essay Scoring in the Presence of Biased Ratings. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 229–237. https://doi.org/10.18653/v1/N18-1021

Arstein, R. and Poesio, M. (2008). Inter-Coder Agreement for Computational Linguistics. Computational Linguistics, Volume 34, Number 4, 555-596. https://doi.org/10.1162/coli.07-034-R2

Conijn, R., Kahr, P., & Snijders, C. (2023). The Effects of Explanations in Automated Essay Scoring Systems on Student Trust and Motivation. Journal of Learning Analytics, 10(1), Article 1. https://doi.org/10.18608/jla.2023.7801

Congdon, P., & MeQueen, J. (2000). The Stability of Rater Severity in Large‐Scale Assessment 

Programs. Journal of Educational Measurement, 37, 163-178. https://doi.org/10.1111/j.1745-3984.2000.tb01081.x. 

Ilievski, I. (2015) Java End-to-End PDTB-Styled Discourse Parser. https://github.com/WING-NUS/pdtb-parser 

Klebanov, B. B., & Madnani, N. (2022). Automated Essay Scoring. Springer International Publishing. https://doi.org/10.1007/978-3-031-02182-4

Lin, Z., Ng, H. T., & Kan, M.-Y. (2014). A PDTB-styled end-to-end discourse parser. Natural Language Engineering, 20(2), 151–184. https://doi.org/10.1017/S1351324912000307

Marro, S., Cabrio, E., & Villata, S. (2022). Graph Embeddings for Argumentation Quality Assessment. Findings of the Association for Computational Linguistics: EMNLP 2022, 4154–4164. https://doi.org/10.18653/v1/2022.findings-emnlp.306

Mikolov, T., Chen, K., Corrado, G.S., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1301.3781

Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 55–60. https://doi.org/10.3115/v1/P14-5010

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830.

Stab, C., & Gurevych, I. (2017). Parsing Argumentation Structures in Persuasive Essays. Computational Linguistics, 43(3), 619–659. https://doi.org/10.1162/COLI_a_00295

Wachsmuth, H., & Werner, T. (2020). Intrinsic Quality Assessment of Arguments. Proceedings of the 28th International Conference on Computational Linguistics, 6739–6745. https://doi.org/10.18653/v1/2020.coling-main.592

Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., & Leimeister, J. M. (2020). AL: An Adaptive Learning Support System for Argumentation Skills. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376732