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English(EN) Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis

LLM通过新的PLCD框架增强在线学习中的认知诊断

研究人员开发了一个名为过程感知语言认知诊断(PLCD)的新框架,该框架利用大型语言模型(LLM)来改进在线学习中的认知诊断。与使用离散学生ID的传统方法不同,PLCD结合了语言衍生的结构和响应记录,以更好地表示学生知识和预测表现。该框架构建概念模式和认知过程图,并使用语义记忆来检索相关的历史响应。实验表明,PLCD优于现有基线,并表现出强大的认知迁移能力,这表明LLM可以增强潜在知识状态的测量。 AI

影响 这项研究通过改进对学生知识的评估方式,有望带来更个性化和有效的在线学习体验。

排序理由 详细介绍使用LLM进行认知诊断新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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LLM通过新的PLCD框架增强在线学习中的认知诊断

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详细介绍使用LLM进行认知诊断新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Minghang Liu, Yuanzhuo Wang, Qiang Qiu, Huawei Shen, Xueqi Cheng ·

    超越ID嵌入:面向认知诊断的基于过程的语言建模

    arXiv:2609.12403v1 Announce Type: cross 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 o…