Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15013 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908457264742400 |
|---|---|
| author | Li, Xiaoyu Wu, Jin Guo, Shaoyang Shi, Haoran Zheng, Chanjin |
| author_facet | Li, Xiaoyu Wu, Jin Guo, Shaoyang Shi, Haoran Zheng, Chanjin |
| contents | In the smart era, psychometric tests are becoming increasingly important for personnel selection, career development, and mental health assessment. Forced-choice tests are common in personality assessments because they require participants to select from closely related options, lowering the risk of response distortion. This study presents a deep learning-based Forced-Choice Neural Cognitive Diagnostic Model (FCNCD) that overcomes the limitations of traditional models and is applicable to the three most common item block types found in forced-choice tests. To account for the unidimensionality of items in forced-choice tests, we create interpretable participant and item parameters. We model the interactions between participant and item features using multilayer neural networks after mining them using nonlinear mapping. In addition, we use the monotonicity assumption to improve the interpretability of the diagnostic results. The FCNCD's effectiveness is validated by experiments on real-world and simulated datasets that show its accuracy, interpretability, and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Forced-Choice Neural Cognitive Diagnostic Model of Personality Testing Li, Xiaoyu Wu, Jin Guo, Shaoyang Shi, Haoran Zheng, Chanjin Artificial Intelligence In the smart era, psychometric tests are becoming increasingly important for personnel selection, career development, and mental health assessment. Forced-choice tests are common in personality assessments because they require participants to select from closely related options, lowering the risk of response distortion. This study presents a deep learning-based Forced-Choice Neural Cognitive Diagnostic Model (FCNCD) that overcomes the limitations of traditional models and is applicable to the three most common item block types found in forced-choice tests. To account for the unidimensionality of items in forced-choice tests, we create interpretable participant and item parameters. We model the interactions between participant and item features using multilayer neural networks after mining them using nonlinear mapping. In addition, we use the monotonicity assumption to improve the interpretability of the diagnostic results. The FCNCD's effectiveness is validated by experiments on real-world and simulated datasets that show its accuracy, interpretability, and robustness. |
| title | A Forced-Choice Neural Cognitive Diagnostic Model of Personality Testing |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15013 |