Automated Essay Scoring Incorporating Annotations from Automated Feedback Systems
This addresses automated essay scoring for educational assessment, but appears incremental as it builds on existing annotation and scoring methods.
This study tackled automated essay scoring by incorporating feedback-oriented annotations for spelling/grammar errors and argumentative components, demonstrating performance improvements using fine-tuned encoder-based large language models.
This study illustrates how incorporating feedback-oriented annotations into the scoring pipeline can enhance the accuracy of automated essay scoring (AES). This approach is demonstrated with the Persuasive Essays for Rating, Selecting, and Understanding Argumentative and Discourse Elements (PERSUADE) corpus. We integrate two types of feedback-driven annotations: those that identify spelling and grammatical errors, and those that highlight argumentative components. To illustrate how this method could be applied in real-world scenarios, we employ two LLMs to generate annotations -- a generative language model used for spell correction and an encoder-based token-classifier trained to identify and mark argumentative elements. By incorporating annotations into the scoring process, we demonstrate improvements in performance using encoder-based large language models fine-tuned as classifiers.