用AI分析考生反馈，自动识别有问题的考题。
Analyzing Examinee Comments using DistilBERT and Machine Learning to Ensure Quality Control in Exam Content
- 用DistilBERT和机器学习模型自动识别负面评语。
- 结合心理测量特征后模型准确率提升，效果优于传统方法。
- 适合考试机构用于提升题目质量，减少人工审核量。
本研究探索使用自然语言处理（NLP）分析考生评论，以识别有问题的试题。我们开发并验证了机器学习模型，可自动识别相关负面反馈，评估了引入心理测量特征对模型性能的提升效果，并比较了NLP标记的题目与传统标记方法的结果。结果显示，考生反馈为统计方法提供了有价值的补充信息，有助于提升测试效度，同时减轻人工审查负担。该研究为考试组织提供了一种高效机制，将考生实际体验纳入质量保证流程。
原文摘要 · Abstract (English)
This study explores using Natural Language Processing (NLP) to analyze candidate comments for identifying problematic test items. We developed and validated machine learning models that automatically identify relevant negative feedback, evaluated approaches of incorporating psychometric features enhances model performance, and compared NLP-flagged items with traditionally flagged items. Results demonstrate that candidate feedback provides valuable complementary information to statistical methods, potentially improving test validity while reducing manual review burden. This research offers testing organizations an efficient mechanism to incorporate direct candidate experience into quality assurance processes.
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