解决跨领域冷启动知识追踪难题，用专家混合与对抗网络提升小样本表现。
Adaptive Knowledge Transfer for Cross-Disciplinary Cold-Start Knowledge Tracing
- 通过聚类构建学生知识状态的属性桥接，实现跨领域的知识迁移。
- 在20个极端冷启动场景中，准确率显著优于现有方法。
- 适合缺乏数据的教育系统跨领域建模，尤其适用于新学科推广。
跨领域冷启动知识追踪（CDCKT）面临核心挑战：目标领域学生交互数据不足，难以建模知识状态并预测性能。现有方法依赖领域间重叠实体进行简单映射的知识迁移，但存在两大局限：(1) 实际场景中重叠实体稀少；(2) 简单映射无法捕捉跨领域知识复杂性。为此，我们提出基于专家混合与对抗生成网络的跨领域冷启动知识追踪框架。方法包含三部分：首先，预训练源领域模型，并将学生知识状态聚类为K类；其次，利用聚类属性通过门控机制引导专家混合网络，作为跨域映射桥梁；第三，对抗判别器通过拉近同属性学生特征、推开异属性特征，有效缓解小样本问题。我们在20个极端跨领域冷启动场景中验证了该方法的有效性。
原文摘要 · Abstract (English)
Cross-Disciplinary Cold-start Knowledge Tracing (CDCKT) faces a critical challenge: insufficient student interaction data in the target discipline prevents effective knowledge state modeling and performance prediction. Existing cross-disciplinary methods rely on overlapping entities between disciplines for knowledge transfer through simple mapping functions, but suffer from two key limitations: (1) overlapping entities are scarce in real-world scenarios, and (2) simple mappings inadequately capture cross-disciplinary knowledge complexity. To overcome these challenges, we propose Mixed of Experts and Adversarial Generative Network-based Cross-disciplinary Cold-start Knowledge Tracing Framework. Our approach consists of three key components: First, we pre-train a source discipline model and cluster student knowledge states into K categories. Second, these cluster attributes guide a mixture-of-experts network through a gating mechanism, serving as a cross-domain mapping bridge. Third, an adversarial discriminator enforces feature separation by pulling same-attribute student features closer while pushing different-attribute features apart, effectively mitigating small-sample limitations. We validate our method's effectiveness across 20 extreme cross-disciplinary cold-start scenarios.
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