Computerized Adaptive Testing (CAT) aims to select the most appropriate questions based on the examinee's ability and is widely used in online education. However, existing CAT systems often lack initial understanding of the examinee's ability, requiring random probing questions. This can lead to poorly matched questions, extending the test duration and negatively impacting the examinee's mindset, a phenomenon referred to as the Cold Start with Insufficient Prior (CSIP) task. This issue occurs because CAT systems do not effectively utilize the abundant prior information about the examinee available from other courses on online platforms. These response records, due to the commonality of cognitive states across different knowledge domains, can provide valuable prior information for the target domain. However, no prior work has explored solutions for the CSIP task. In response to this gap, we propose Diffusion Cognitive States TransfeR Framework (DCSR), a novel domain transfer framework based on Diffusion Models (DMs) to address the CSIP task. Specifically, we construct a cognitive state transition bridge between domains, guided by the common cognitive states of examinees, encouraging the model to reconstruct the initial ability state in the target domain. To enrich the expressive power of the generated data, we analyze the causal relationships in the generation process from a causal perspective. Redundant and extraneous cognitive states can lead to limited transfer and negative transfer effects. Our DCSR can seamlessly apply the generated initial ability states in the target domain to existing question selection algorithms, thus improving the cold start performance of the CAT system. Extensive experiments conducted on five real-world datasets demonstrate that DCSR significantly outperforms existing baseline methods in addressing the CSIP task.

翻译：计算机化自适应测试（CAT）旨在根据被试者的能力选择最合适的问题，广泛应用于在线教育。然而，现有的CAT系统往往缺乏对被试者能力的初始理解，需要随机探测问题。这可能导致问题匹配不当，延长测试时间并对被试者的心态产生负面影响，这种现象被称为先验不足冷启动（CSIP）任务。该问题的出现是因为CAT系统未能有效利用在线平台上其他课程中可获得的关于被试者的丰富先验信息。这些响应记录由于不同知识领域间认知状态的共通性，可以为目标领域提供有价值的先验信息。然而，先前的研究尚未探索CSIP任务的解决方案。针对这一空白，我们提出了扩散认知状态迁移框架（DCSR），一种基于扩散模型（DMs）的新型领域迁移框架，以解决CSIP任务。具体而言，我们在领域间构建了一个认知状态转移桥梁，以被试者的共同认知状态为指导，鼓励模型重建目标领域的初始能力状态。为了增强生成数据的表达能力，我们从因果角度分析了生成过程中的因果关系。冗余和无关的认知状态可能导致有限的迁移和负迁移效应。我们的DCSR可以将生成的目标领域初始能力状态无缝应用于现有的问题选择算法，从而提升CAT系统的冷启动性能。在五个真实数据集上进行的大量实验表明，DCSR在解决CSIP任务方面显著优于现有的基线方法。