用学生行为数据构建认知画像，更准预测多选题难度
MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
- 基于学生交互数据提取行为人格，替代传统能力假设
- 模型在5折交叉验证中误差降低25%，解释力提升至0.686
- 发现可解释的人格类型，适合教育诊断与题目优化
预测多选题难度对有效评估至关重要，但现有方法常假设学生能力分布为单峰，忽视了学生误解的异质性。本文提出一种基于人格的框架，将理论能力采样替换为数据驱动的认知画像。利用EEDI数据集中的学生交互数据，通过潜在类别分析（LCA）识别行为人格，再让大语言模型（LLM）为每种人格模拟答题分布。这些信号与题目主题上下文结合，输入岭回归模型以预测项目反应理论（IRT）难度参数。五折交叉验证显示，本方法相较近期基线显著提升：均方误差由0.367降至0.274，决定系数从0.525升至0.686。所发现的人格具有可解释性，能揭示题目难的原因，对诊断性评估设计具应用潜力。
原文摘要 · Abstract (English)
Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal student ability distribution, overlooking the heterogeneous nature of student misconceptions. We propose a persona-driven framework that replaces theoretical ability sampling with data-driven cognitive profiling. Using student interactions from the EEDI dataset, we identify behavioral personas via latent class analysis (LCA), then condition a large language model (LLM) to simulate response distributions for each persona. These signals are aggregated with topic context and fed into a Ridge Regression model to predict the item response theory (IRT) difficulty parameter. With five-fold cross-validation, our method improves over a recent baseline (MSE: 0.367 to 0.274; R2: 0.525 to 0.686). The discovered personas are interpretable and offer insights into why items are difficult, with potential applications to diagnostic assessment design.
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