Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis

2026-09-10

AI Summary

本文提出了一种基于过程的语言认知诊断框架（PLCD），利用大型语言模型和响应记录来改进学生知识状态的预测，解决了传统认知诊断模型在新练习或概念出现时的语义限制问题。

Abstract

Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based embeddings to represent students, exercises, and concepts. This paradigm diverges from the nature of learner cognition, where knowledge is not stored and retrieved as isolated symbols. As a result, CDMs suffer from semantic limitations when new exercises or concepts appear. In this paper, we propose a Process-aware Language Cognitive Diagnosis (PLCD) framework that uses language-derived structures as cognitive priors and response records to calibrate student posterior states. PLCD leverages large language models (LLMs) to construct concept schemas and cognitive process graphs, and uses target-conditioned semantic memory to retrieve historical responses that are relevant to each target exercise. A process-grounded Language-to-Cognition Mapper with DA-MoE experts and process-level contrastive learning then maps the textual evidence into a unified cognitive space. Experimental results show that PLCD not only outperforms traditional baselines in predicting student performance but also exhibits strong cognitive transfer capabilities. These results connect the computational power of LLMs with the psychometric goal of measuring latent knowledge states, suggesting that structured language priors calibrated by response records can improve cold-start robustness and cognitive grounding.

Problem

• Cognitive Diagnosis Models
• ID-based embeddings
• learner cognition
• semantic limitations
• new exercises

Innovation

• Process-aware Language Cognitive Diagnosis (PLCD)
• Large Language Models (LLMs)
• Concept Schemas
• Cognitive Process Graphs
• DA-MoE Experts