Cognitive diagnosis aims to gauge students' mastery levels based on their response logs. Serving as a pivotal module in web-based online intelligent education systems (WOIESs), it plays an upstream and fundamental role in downstream tasks like learning item recommendation and computerized adaptive testing. WOIESs are open learning environment where numerous new students constantly register and complete exercises. In WOIESs, efficient cognitive diagnosis is crucial to fast feedback and accelerating student learning. However, the existing cognitive diagnosis methods always employ intrinsically transductive student-specific embeddings, which become slow and costly due to retraining when dealing with new students who are unseen during training. To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs. Specifically, in ICDM, we propose a novel student-centered graph (SCG). Rather than inferring mastery levels through updating student-specific embedding, we derive the inductive mastery levels as the aggregated outcomes of students' neighbors in SCG. Namely, SCG enables to shift the task from finding the most suitable student-specific embedding that fits the response logs to finding the most suitable representations for different node types in SCG, and the latter is more efficient since it no longer requires retraining. To obtain this representation, ICDM consists of a construction-aggregation-generation-transformation process to learn the final representation of students, exercises and concepts. Extensive experiments across real-world datasets show that, compared with the existing cognitive diagnosis methods that are always transductive, ICDM is much more faster while maintains the competitive inference performance for new students.

翻译：认知诊断旨在根据学生的作答记录评估其掌握程度。作为网络在线智能教育系统的核心模块，它在学习项目推荐和计算机自适应测试等下游任务中发挥着上游基础性作用。网络在线智能教育系统是一种开放式学习环境，大量新学生会持续注册并完成练习。在该系统中，高效的认知诊断对于快速反馈和加速学生学习至关重要。然而，现有认知诊断方法通常采用本质上基于转导的学生特定嵌入表示，当需要处理训练阶段未见过的学生时，必须重新训练模型，导致速度缓慢且计算成本高昂。为此，本文提出了一种归纳式认知诊断模型（ICDM），用于在网络在线智能教育系统中快速推断新学生的掌握水平。具体而言，在ICDM中，我们提出了一种新颖的学生中心图（SCG）。通过SCG，我们无需通过更新学生特定嵌入来推断掌握水平，而是将归纳式掌握水平推导为SCG中学生邻居的聚合结果。也就是说，SCG能够将任务从寻找最适合作答记录的学生特定嵌入转换为寻找SCG中不同节点类型的最优表示，后者无需重新训练，因此效率更高。为获取这种表示，ICDM通过构建-聚合-生成-转换过程来学习学生、习题和概念的最终表示。基于真实数据集的广泛实验表明，与现有通常基于转导的认知诊断方法相比，ICDM在保持对新生具有竞争力的推断性能的同时，计算速度显著提升。