Just Read the Question: Enabling Generalization to New Assessment Items with Text Awareness
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915382735929344 |
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| author | Khan, Arisha Li, Nathaniel Shen, Tori Rafferty, Anna N. |
| author_facet | Khan, Arisha Li, Nathaniel Shen, Tori Rafferty, Anna N. |
| contents | Machine learning has been proposed as a way to improve educational assessment by making fine-grained predictions about student performance and learning relationships between items. One challenge with many machine learning approaches is incorporating new items, as these approaches rely heavily on historical data. We develop Text-LENS by extending the LENS partial variational auto-encoder for educational assessment to leverage item text embeddings, and explore the impact on predictive performance and generalization to previously unseen items. We examine performance on two datasets: Eedi, a publicly available dataset that includes item content, and LLM-Sim, a novel dataset with test items produced by an LLM. We find that Text-LENS matches LENS' performance on seen items and improves upon it in a variety of conditions involving unseen items; it effectively learns student proficiency from and makes predictions about student performance on new items. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08154 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Just Read the Question: Enabling Generalization to New Assessment Items with Text Awareness Khan, Arisha Li, Nathaniel Shen, Tori Rafferty, Anna N. Machine Learning Machine learning has been proposed as a way to improve educational assessment by making fine-grained predictions about student performance and learning relationships between items. One challenge with many machine learning approaches is incorporating new items, as these approaches rely heavily on historical data. We develop Text-LENS by extending the LENS partial variational auto-encoder for educational assessment to leverage item text embeddings, and explore the impact on predictive performance and generalization to previously unseen items. We examine performance on two datasets: Eedi, a publicly available dataset that includes item content, and LLM-Sim, a novel dataset with test items produced by an LLM. We find that Text-LENS matches LENS' performance on seen items and improves upon it in a variety of conditions involving unseen items; it effectively learns student proficiency from and makes predictions about student performance on new items. |
| title | Just Read the Question: Enabling Generalization to New Assessment Items with Text Awareness |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.08154 |