发表机构
Shenzhen University; Guangdong Polytechnic Normal University（深圳大学; 广东技术师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对增量认知诊断中概念空间扩展导致的历史诊断不一致问题，提出CLEAN框架，通过架构隔离与正交掩码实现零表示漂移，保证心理测量学一致性并提升新题性能。
AI 中文摘要
认知诊断（CD）是智能教育中的一项基础任务，用于刻画学习者对知识概念的掌握程度。在真实学习平台中，新加入的题目会不断引入之前未见过的概念，从而需要动态扩展底层概念空间。然而，现有的增量认知诊断模型假设概念空间固定不变，导致新题目的梯度会覆盖历史路径并引发灾难性遗忘。更为关键的是，这些方法仅依赖软约束来保留历史诊断结果，此类约束可能无法满足增量更新后诊断不变性的要求，即认知诊断中的心理测量学一致性。为此，我们提出了CLEAN（可扩展且架构隔离网络的持续学习），一种新颖的增量认知诊断框架，支持概念空间扩展，同时为历史诊断的点态不变性提供结构性保证。具体而言，CLEAN首先引入严格的拓扑二分协议，冻结历史诊断函数，并应用确定性正交列掩码来切断梯度干扰。其次，为适应概念扩展，部署具有微方差初始化的可扩展满秩分支以学习新概念。最后，为验证该架构设计通过构造实现不变性，我们形式化了表示漂移（RD）以量化历史特征的扰动。在三个大规模教育数据集上的广泛实验表明，CLEAN实现了零RD，通过架构隔离使旧题指标与静态锚点完全一致，同时在新题上保持与强持续学习基线相当或更优的性能。
英文摘要
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, necessitating dynamic expansion of the underlying concept space. Yet existing incremental CD models assume a fixed concept space, allowing gradients from new items to overwrite historical pathways and induce catastrophic forgetting. More critically, these methods rely solely on soft constraints to preserve historical diagnoses. Such constraints may fail to satisfy the requirement of diagnostic invariance after incremental updates, a requirement known as psychometric consistency in cognitive diagnosis. Therefore, we propose CLEAN (Continual Learning with Expandable and Architecturally Isolated Networks), a novel incremental CD framework supporting concept-space expansion while providing structural guarantees for pointwise invariance of historical diagnoses. Specifically, CLEAN first introduces a strict topological bipartition protocol, freezes historical diagnostic functions and applies deterministic orthogonal column masking to sever gradient interference. Second, to accommodate concept expansion, expandable full-rank branches with micro-variance initialization are deployed to learn novel concepts. Finally, to verify that this architectural design achieves invariance by construction, we formalize Representation Drift (RD) to quantify the perturbation of historical traits. Extensive experiments on three large-scale educational datasets demonstrate that CLEAN achieves zero RD, preserving old-item metrics identically to static anchors through architectural isolation while remaining competitive with or superior to strong continual-learning baselines on new items.
Comments11 pages, 7 figures
