基于模拟响应概率的多模态题目参数估计
Multimodal Item Parameter Estimation using Simulated Response Probabilitie
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中文总结 AI 辅助
本研究基于Qwen3.5微调多模态大语言模型，利用含图像与文本的多项选择题训练语料，通过学习学生能力相关的选择概率，实现了题目参数的准确估计。
中文摘要 AI 辅助
我们展示了使用基于Qwen3.5微调的多模态大语言模型（LLM）重构多项选择模型（MCM）和三参数逻辑（3PL）模型曲线的结果。该模型经过提示和微调，以复制包含图像与文本刺激的大量多项选择题训练语料库中的选择概率，且以带标签的学生能力水平集为条件。通过学习再现学生在离散能力范围内的系统误差模式，该LLM隐式捕获了3PL和MCM曲线中编码的潜在响应概率，这使我们能直接从模型预测的选项概率中准确估计保留测试集上的题目难度。
英文摘要
We present results from reconstructing multiple-choice model (MCM) and three-parameter logistic (3PL) model curves using a fine-tuned multimodal large language model (LLM) based on Qwen3.5. The model is prompted and fine-tuned to replicate choice probabilities across a large training corpus of multiple-choice items containing both image and text stimuli, conditioned on a labeled set of student ability levels. By learning to reproduce the systematic error patterns of students across a discrete range of abilities, the LLM implicitly captures the underlying response probabilities encoded in the 3PL and MCM curves. This allows us to accurately approximate item difficulty on a held-out test set directly from the model's predicted option probabilities.
发表机构
- College Board（大学理事会）
机构由 AI 辅助整理，请以论文原文为准。
