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Significant progress has been made in deep-learning based Automatic Essay Scoring (AES) systems in the past two decades.
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Powers, D.E., Burstein, J.C., Chodorow, M., Fowles, M.E., Kukich, K.: Stumping e-rater: challenging the validity of automated essay scoring. Computers in Human Behavior 18
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ASAP-AES: The hewlett foundation: Automated essay scoring develop an automated scoring algorithm for student-written essays. https://www.kaggle.com/c/asap-aes/ (2012)
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EASE, E.: Ease (enhanced ai scoring engine) is a library that allows for machine learning based classification of textual content. this is useful for tasks such as scoring student essays. https://github.com/edx/ease (2013)
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Perelman, L.: When “the state of the art” is counting words. Assessing Writing 21
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Greene, P.: Automated essay scoring remains an empty dream. https://www.forbes.com/sites/petergreene/2018/07/02/automated-essay-scoring-remains-an-empty-dream/?sh=da976a574b91 (2018)
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Smith, T.: More states opting to ’robo-grade’ student essays by computer. https://www.npr.org/2018/06/30/624373367/more-states-opting-to-robo-grade-student-essays-by-computer (2018)
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Tay, Y., Phan, M.C., Tuan, L.A., Hui, S.C.: Skipflow: Incorporating neural coherence features for end-to-end automatic text scoring. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
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Perelman, L., Sobel, L., Beckman, M., Jiang, D.: Basic automatic b.s. essay language generator (babel) by les perelman, ph.d. http://lesperelman.com/writing-assessment-robo-grading/babel-generator/ (2014)
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Taghipour, K., Ng, H.T.: A neural approach to automated essay scoring. In: Proceedings of the 2016 conference on empirical methods in natural language processing. pp. 1882–1891 (2016)
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Mid-Day: What?! students write song lyrics and abuses in exam answer sheet. https://www.mid-day.com/articles/national-news-west-bengal-students-write-film-song-lyrics-abuses-in-exam-answer-sheet/18210196 (2017)
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Zhao, S., Zhang, Y., Xiong, X., Botelho, A., Heffernan, N.: A memory-augmented neural model for automated grading. In: Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale. pp. 189–192 (2017)
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Yoon, S.Y., Cahill, A., Loukina, A., Zechner, K., Riordan, B., Madnani, N.: Atypical inputs in educational applications. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers). pp. 60–67 (2018)
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Feathers, T.: Flawed algorithms are grading millions of students’ essays. https://www.vice.com/en/article/pa7dj9/flawed-algorithms-are-grading-millions-of-students-essays (2019)
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Kumar, Y., Aggarwal, S., Mahata, D., Shah, R.R., Kumaraguru, P., Zimmermann, R.: Get it scored using autosas—an automated system for scoring short answers. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 9662–9669 (2019)
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Yan, D., Rupp, A.A., Foltz, P.W.: Handbook of automated scoring: Theory into practice. CRC Press (2020)
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