让推理可见：面向结构化推理任务的配置感知诊断建模研究
Making Reasoning Visible: Configuration-Aware Diagnostic Modeling for Adaptive Scaffolding in Structured Reasoning Tasks
AI 导读
一项研究提出诊断框架，通过分析学生在交互式学习游戏中的动作序列，使逐步推理过程可见，并以语法推理为实例验证。基于 234 名学生在 42 道形态句法练习中的 36,234 次交互，研究发现推理策略主要随情境变化（组内相关系数 0.14），74.6% 由高协调需求导致的失败在无干预时会再次失败。早期预警模型预测验证成功达 AUC 0.826，加入序列上下文后达 0.902。
正文 · 原文
Abstract:Modeling the latent, multi-step reasoning processes of learners is a core challenge for adaptive tutoring systems, which often rely on simple right/wrong answers rather than understanding how students think. Instantiated here for grammatical reasoning, this study introduces a diagnostic framework that makes students' step-by-step problem-solving visible by analyzing their action sequences in an interactive learning game. The framework computes five interpretable strategy dimensions (e.g., systematic error correction, random guessing), estimates coordination demand from task structure and behavioral patterns, and classifies reasoning into four distinct states. Analyzing 36,234 interactions from 234 students working on 42 morphosyntactic exercises, we found that reasoning strategies vary primarily by context rather than stable traits (intraclass correlation = 0.14), and students show high persistence in unproductive states; specifically, 74.6% of failures driven by high coordination demand (rather than simple knowledge gaps) are followed by another failure without intervention. Our early warning model predicts validation success with strong accuracy (AUC = 0.826 using strategy patterns alone; 0.902 with additional sequential context) while avoiding circular reliance on current error counts. These findings demonstrate that making latent reasoning processes visible enables earlier, more targeted scaffolding that responds to how students are thinking rather than simply whether they succeed. While demonstrated in language learning, the framework provides a generalizable, interpretable architecture for diagnosing complex, structured reasoning in any domain.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.35609 [cs.CL] |
| (or arXiv:2610.35609v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35609 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Trystan Geoffre [view email]
[v1]
Mon, 26 Oct 2026 15:30:52 UTC (1,658 KB)
来源：arXiv cs.AI · arxiv.org