Just Read the Question: Enabling Generalization to New Assessment Items with Text Awareness
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Arisha Khan, Nathaniel Li, Tori Shen, Anna Rafferty
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6893483554
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