基于心理测量学的作文评分与自适应反馈框架
PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback
- 用神经网络结合项目反应理论，精准评估学生能力
- 根据能力水平动态生成教学引导反馈，提升指导针对性
- 支持教育场景下的可解释评分与有效反馈，适合教学研究者
高效的自动作文评分系统需兼顾可靠评估与可操作的教学反馈。现有方法常将评分与反馈割裂：神经评分模型可解释性弱，大语言模型生成的反馈又缺乏对学习者水平的敏感性。为此，本文提出PsyScore，一个融合诊断评估与教学支架的联合框架，通过共享的能力潜变量实现一体化设计。该框架包含三个模块：1）基于分级部分计分模型（GPCM）的特质自适应神经项目反应模型，兼具精确能力估计与心理测量可解释性；2）基于最近发展区（ZPD）的多智能体反馈生成器，依据诊断出的能力参数动态调整教学重点；3）多视角反馈评估策略，通过成对偏好判断和学生修订模拟衡量反馈质量。在ASAP++数据集上的实验表明，PsyScore在评分性能上表现良好，同时提供更具教学意义的反馈。
原文摘要 · Abstract (English)
Effective Automated Essay Scoring (AES) are expected to support both reliable assessment and actionable instructional feedback. However, existing approaches often treat scoring and feedback as separate components: neural scoring models provide limited interpretability, while Large Language Model (LLM)-based feedback is typically insensitive to learners proficiency levels. To address this fragmentation, this work proposes PsyScore, a psychometrically-aware framework that integrates diagnostic assessment with instructional scaffolding through a shared latent ability representation. PsyScore comprises three key modules: a Trait-Adaptive Neural IRT Scorer that incorporates the Graded Partial Credit Model (GPCM) into a neural architecture, enabling the precise estimation of student ability while maintaining psychometric interpretability, a ZPD-Scaffolded Feedback Generator, which conditions multi-agent feedback strategies on the diagnosed ability parameter to adapt instructional focus across different proficiency levels, and a Multi-Perspective Feedback Evaluation Strategy that assesses feedback quality via pairwise preference judgements and student revision simulations. Experiments on the ASAP++ dataset demonstrate that PsyScore achieves competitive scoring performance while providing more pedagogically aligned feedback.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。