Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts. Despite the effectiveness of existing efforts, previous methods always considered the mastery level on the whole students, so they still suffer from the Long Tail Effect. A large number of students who have sparse data are performed poorly in the model. To relieve the situation, we proposed a Self-supervised Cognitive Diagnosis (SCD) framework which leverages the self-supervised manner to assist the graph-based cognitive diagnosis, then the performance on those students with sparse data can be improved. Specifically, we came up with a graph confusion method that drops edges under some special rules to generate different sparse views of the graph. By maximizing the consistency of the representation on the same node under different views, the model could be more focused on long-tailed students. Additionally, we proposed an importance-based view generation rule to improve the influence of long-tailed students. Extensive experiments on real-world datasets show the effectiveness of our approach, especially on the students with sparse data.
翻译:认知诊断是智能教育领域一项基本而关键的研究任务,目的是发现不同学生在特定知识概念方面的熟练程度。尽管现有努力取得了成效,但以往的方法总是考虑到整个学生的掌握水平,因此他们仍然受到长尾效应的影响。许多缺乏数据的学生在模型中表现不佳。为了缓解这种情况,我们提议了一个自我监督的认知诊断(SCD)框架,利用自我监督的方式协助基于图表的认知诊断,然后可以提高那些数据稀少的学生的成绩。具体地说,我们制定了一种图形混乱方法,根据某些特殊规则,在图表中跳出边缘,产生不同的观点。通过在不同观点下最大限度地统一同一节点上的代表性,模型可以更加侧重于长尾学生。此外,我们提出了一种基于重要观点的生成规则,以改善长尾学生的影响。关于真实世界数据集的广泛实验显示了我们的方法的有效性,特别是对缺乏数据的学生。