Transfer Learning for Automated Feedback Generation on Small Datasets
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916653395083264 |
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| author | Morris, Oscar |
| author_facet | Morris, Oscar |
| contents | Feedback is a very important part the learning process. However, it is challenging to make this feedback both timely and accurate when relying on human markers. This is the challenge that Automated Feedback Generation attempts to address. In this paper, a technique to train such a system on a very small dataset with very long sequences is presented. Both of these attributes make this a very challenging task, however, by using a three stage transfer learning pipeline state-of-the-art results can be achieved with qualitatively accurate but unhuman sounding results. The use of both Automated Essay Scoring and Automated Feedback Generation systems in the real world is also discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning for Automated Feedback Generation on Small Datasets Morris, Oscar Computation and Language Artificial Intelligence Feedback is a very important part the learning process. However, it is challenging to make this feedback both timely and accurate when relying on human markers. This is the challenge that Automated Feedback Generation attempts to address. In this paper, a technique to train such a system on a very small dataset with very long sequences is presented. Both of these attributes make this a very challenging task, however, by using a three stage transfer learning pipeline state-of-the-art results can be achieved with qualitatively accurate but unhuman sounding results. The use of both Automated Essay Scoring and Automated Feedback Generation systems in the real world is also discussed. |
| title | Transfer Learning for Automated Feedback Generation on Small Datasets |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.11836 |