用对话框架提升学生认知诊断准确率
DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework
- 基于教育理论的IRE框架设计对话诊断结构
- 图编码融合问题与知识点，提升信息捕捉精度
- 首个面向对话的认知诊断模型，结果更可解释
尽管认知诊断（CD）能有效评估学生在结构化测试中的知识掌握情况，但将其应用于真实师生对话仍面临两大挑战：传统CD模型缺乏处理动态非结构化对话的框架，且难以从长对话中准确提取诊断语义。为此，我们提出DiaCDM，创新性地引入教育学中的发起-回应-评价（IRE）框架，构建适配对话场景的诊断结构，并设计了一种独特的图编码方法，将教师提问与相关知识组件融合，更精准捕捉关键信息。据我们所知，这是首个探索对话环境下认知诊断的研究。在三个真实对话数据集上的实验表明，DiaCDM不仅显著提升诊断准确率，还增强结果可解释性，为教师提供有力的认知状态评估工具。代码已开源。
原文摘要 · Abstract (English)
While cognitive diagnosis (CD) effectively assesses students' knowledge mastery from structured test data, applying it to real-world teacher-student dialogues presents two fundamental challenges. Traditional CD models lack a suitable framework for handling dynamic, unstructured dialogues, and it's difficult to accurately extract diagnostic semantics from lengthy dialogues. To overcome these hurdles, we propose DiaCDM, an innovative model. We've adapted the initiation-response-evaluation (IRE) framework from educational theory to design a diagnostic framework tailored for dialogue. We also developed a unique graph-based encoding method that integrates teacher questions with relevant knowledge components to capture key information more precisely. To our knowledge, this is the first exploration of cognitive diagnosis in a dialogue setting. Experiments on three real-world dialogue datasets confirm that DiaCDM not only significantly improves diagnostic accuracy but also enhances the results' interpretability, providing teachers with a powerful tool for assessing students' cognitive states. The code is available at https://github.com/Mind-Lab-ECNU/DiaCDM/tree/main.
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