English(EN) CLEAN: Psychometrically Consistent Incremental Cognitive Diagnosis under Concept-Space Expansion via Architectural Isolation

新的CLEAN框架确保概念空间演变中的认知诊断一致性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员开发了CLEAN（Continual Learning with Expandable and Architecturally Isolated Networks，可扩展和架构隔离网络的持续学习）框架，这是一种用于认知诊断的新框架，可解决增量学习场景中的灾难性遗忘问题。该方法通过在架构上将新概念学习与历史诊断功能隔离开来，防止梯度干扰，从而确保心理测量一致性。在教育数据集上的实验表明，CLEAN实现了零表示漂移，在旧项目上的性能与静态模型相同，同时在新概念上保持竞争力。 AI

影响该框架可以通过防止新概念引入时性能下降，来提高人工智能驱动的教育工具的准确性和可靠性。

排序理由这是一篇详细介绍新认知诊断框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tao He, Jinxing Xiang, Fan Jiang · 2026-10-05 04:00

CLEAN：通过架构隔离在概念空间扩展下实现心理测量一致的增量认知诊断

arXiv:2610.02278v1 Announce Type: new Abstract: Cognitive diagnosis (CD) is a fundamental task in intelligent education that profiles learner proficiency over knowledge concepts. In real-world learning platforms, newly added items continually introduce previously unseen concepts,…

本文如何被排名

报道来源 [1]

CLEAN：通过架构隔离在概念空间扩展下实现心理测量一致的增量认知诊断

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