通过数据驱动的认知画像建模学习者异质性以预测多项选择题难度
MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
AI总结：
本文提出基于学习者异质性的数据驱动认知画像框架，通过隐类分析识别行为画像并模拟响应分布，结合主题上下文和岭回归模型预测IRT难度参数，提升难度预测精度。
摘要：
预测多项选择题（MCQ）难度对有效评估至关重要，但当前方法通常假设学生能力分布单峰，忽视学生误解的异质性。本文提出一种基于角色的框架，用数据驱动的认知画像替代理论能力采样。利用EEDI数据集中的学生互动，通过潜在类分析（LCA）识别行为画像，然后将大语言模型（LLM）调制以模拟每个画像的响应分布。这些信号与主题上下文结合，输入岭回归模型预测项目反应理论（IRT）难度参数。通过五折交叉验证，本文方法在MSE上优于最近的基线（0.367到0.274；R2：0.525到0.686）。发现的画像具有可解释性，并提供了关于项目难度原因的见解，潜在应用于诊断评估设计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
