Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915519485968384 |
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| author | Peters, Sydney Zhang, Nan Jiao, Hong Li, Ming Zhou, Tianyi Lissitz, Robert |
| author_facet | Peters, Sydney Zhang, Nan Jiao, Hong Li, Ming Zhou, Tianyi Lissitz, Robert |
| contents | Item difficulty plays a crucial role in test performance, interpretability of scores, and equity for all test-takers, especially in large-scale assessments. Traditional approaches to item difficulty modeling rely on field testing and classical test theory (CTT)-based item analysis or item response theory (IRT) calibration, which can be time-consuming and costly. To overcome these challenges, text-based approaches leveraging machine learning and language models, have emerged as promising alternatives. This paper reviews and synthesizes 37 articles on automated item difficulty prediction in large-scale assessment settings published through May 2025. For each study, we delineate the dataset, difficulty parameter, subject domain, item type, number of items, training and test data split, input, features, model, evaluation criteria, and model performance outcomes. Results showed that although classic machine learning models remain relevant due to their interpretability, state-of-the-art language models, using both small and large transformer-based architectures, can capture syntactic and semantic patterns without the need for manual feature engineering. Uniquely, model performance outcomes were summarized to serve as a benchmark for future research and overall, text-based methods have the potential to predict item difficulty with root mean square error (RMSE) as low as 0.165, Pearson correlation as high as 0.87, and accuracy as high as 0.806. The review concludes by discussing implications for practice and outlining future research directions for automated item difficulty modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23486 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review Peters, Sydney Zhang, Nan Jiao, Hong Li, Ming Zhou, Tianyi Lissitz, Robert Computation and Language Artificial Intelligence I.2.7 I.2.7 Item difficulty plays a crucial role in test performance, interpretability of scores, and equity for all test-takers, especially in large-scale assessments. Traditional approaches to item difficulty modeling rely on field testing and classical test theory (CTT)-based item analysis or item response theory (IRT) calibration, which can be time-consuming and costly. To overcome these challenges, text-based approaches leveraging machine learning and language models, have emerged as promising alternatives. This paper reviews and synthesizes 37 articles on automated item difficulty prediction in large-scale assessment settings published through May 2025. For each study, we delineate the dataset, difficulty parameter, subject domain, item type, number of items, training and test data split, input, features, model, evaluation criteria, and model performance outcomes. Results showed that although classic machine learning models remain relevant due to their interpretability, state-of-the-art language models, using both small and large transformer-based architectures, can capture syntactic and semantic patterns without the need for manual feature engineering. Uniquely, model performance outcomes were summarized to serve as a benchmark for future research and overall, text-based methods have the potential to predict item difficulty with root mean square error (RMSE) as low as 0.165, Pearson correlation as high as 0.87, and accuracy as high as 0.806. The review concludes by discussing implications for practice and outlining future research directions for automated item difficulty modeling. |
| title | Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review |
| topic | Computation and Language Artificial Intelligence I.2.7 I.2.7 |
| url | https://arxiv.org/abs/2509.23486 |