Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing

Tazin Afrin, Diane Litman
2/10/2023
cs.CLcs.AI

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Abstract

We develop models to classify desirable evidence and desirable reasoning revisions in student argumentative writing. We explore two ways to improve classifier performance - using the essay context of the revision, and using the feedback students received before the revision. We perform both intrinsic and extrinsic evaluation for each of our models and report a qualitative analysis. Our results show that while a model using feedback information improves over a baseline model, models utilizing context - either alone or with feedback - are the most successful in identifying desirable revisions.

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Cite this paper

@article{afrin2023predicting,
  title  = {Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing},
  author = {Tazin Afrin and Diane Litman},
  year   = {2023},
  eprint = {2302.05039},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.05039v1}
}

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