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8. A Fairness Evaluation of Automated Methods for Scoring Text Evidence Usage in Writing

Author

Litman, Diane, Zhang, Haoran, Correnti, Richard, Matsumura, Lindsay Clare, and Wang, Elaine

Abstract

Automated Essay Scoring (AES) can reliably grade essays at scale and reduce human effort in both classroom and commercial settings. There are currently three dominant supervised learning paradigms for building AES models: feature-based, neural, and hybrid. While feature-based models are more explainable, neural network models often outperform feature-based models in terms of prediction accuracy. To create models that are accurate and explainable, hybrid approaches combining neural network and feature-based models are of increasing interest. We compare these three types of AES models with respect to a different evaluation dimension, namely algorithmic fairness. We apply three definitions of AES fairness to an essay corpus scored by different types of AES systems with respect to upper elementary students' use of text evidence. Our results indicate that different AES models exhibit different types of biases, spanning students' gender, race, and socioeconomic status. We conclude with a step towards mitigating AES bias once detected. [This paper was published in: "AIED 2021," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 255-67.]

Published
2021
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37. eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

Author

Zhang, H., Magooda, A., Litman, D., Correnti, R., Wang, E., Matsumura, L. C., Howe, E., and Quintana, R.

Abstract

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubric-based essay scoring to trigger formative feedback messages regarding students' use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision. [This paper was published in: "The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19)" (p. 9619-9625). Association for the Advancement of Artificial Intelligence.]

Published
2019

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