ProIQA：一种基于过程的细粒度数学题目质量评估框架
ProIQA: A Process-Based Framework for Fine-Grained Math Item Quality Assessment
- East China Normal University（华东师范大学）
- Shanghai Innovation Institute（上海创新研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出ProIQA，一种基于过程的框架，利用大语言模型构建推理树并用图神经网络编码，从知识概念、难度和学科能力三个维度对数学题目进行细粒度质量评估，实验证明其有效性。
AI中文摘要：
自动题目生成（AIG）对于个性化教育至关重要，然而保证生成题目的教学价值仍然是一个瓶颈。现有的题目质量评估（IQA）方法通常依赖于不可扩展的人工评审或浅层的基于题干（stem）的指标，无法捕捉数学问题求解所需的推理过程。为弥补这一差距，本文提出了基于过程的题目质量评估（ProIQA），一种面向过程的细粒度数学题目质量评估框架。我们首先在统一的过程感知视角下，将IQA表述为三个异构维度，包括知识概念、难度和学科能力。基于这一表述，我们通过用从原始解答中导出的结构化推理树增强原始题目数据，构建了一个过程增强的IQA资源。在技术上，ProIQA利用大型语言模型构建层次化推理树，并采用图神经网络（GNN）对其拓扑依赖和过程语义进行编码。得到的求解表示通过双视图（“题干+求解”）架构与题干语义融合，从而实现对学习目标的全面评估。在K12数学数据集上的大量实验表明，ProIQA有效捕捉了过程导向的特征，为智能教育系统中评估AIG输出提供了一种可扩展的数据驱动解决方案。
英文摘要：
Automatic Item Generation (AIG) is pivotal for personalized education, yet guaranteeing the pedagogical value of generated items remains a bottleneck. Existing Item Quality Assessment (IQA) methods typically rely on unscalable manual reviews or shallow stem-based metrics, failing to capture the reasoning process required for mathematical problem-solving. To bridge this gap, this paper proposes Process-based Item Quality Assessment (ProIQA), a process-aware framework for fine-grained quality assessment of math items. We first formulate IQA across three heterogeneous dimensions, including knowledge concepts, difficulty, and disciplinary competencies, under a unified process-aware perspective. Based on this formulation, we construct a process-enhanced IQA resource by augmenting original item data with structured reasoning trees derived from raw solutions. Technically, ProIQA leverages Large Language Modelsto construct hierarchical reasoning trees and employs Graph Neural Networks (GNN) to encode their topological dependencies and procedural semantics. The resulting solving representation is fused with stem semantics through a dual-view (``Stem + Solving'') architecture, enabling comprehensive assessment across learning objectives. Extensive experiments on K12 mathematical datasets show that ProIQA effectively captures process-oriented features, offering a scalable data-driven solution for evaluating AIG outputs in intelligent education systems.
