基于模糊测度的知识关联性建模方法

在教学应用场景中,知识之间的关联性广受关注,但现有研究通常偏重两两知识点之间关系的建模,忽视知识集合中复杂的关联关系,导致研究结果出现偏差.因此,文中引入模糊测度对知识集合进行量化度量,并在此基础上提出基于模糊测度的知识关联性建模方法.首先,基于认知心理学理论,分析知识间存在的三种不同关系,并利用模糊测度建模知识间的关联性,通过实际教学场景论证方法的实用性.然后,在模糊测度建模的基础上,从知识关联性的视角讨论知识的重要度和交互指标.最后,研究知识关联性在认知诊断中的应用.真实数据集上的实验证实知识关联性对认知诊断的影响,不仅有效提升预测精度,也提供更好的可解释性.

Modeling Method of Knowledge Relevance Based on Fuzzy Measures

The relevance between knowledge in the instructional scenarios draws much attention.The existing research usually focuses on modeling the relationship between two knowledge points.However,the complex relevance in knowledge sets is ignored,which results in the deviation of the research results.Aiming at this problem,the fuzzy measure is introduced to quantify the knowledge set,and then a modeling method of knowledge relevance based on fuzzy measures is proposed.Firstly,three different knowledge relationships are analyzed grounded on the cognitive theory,and the knowledge relevance is modeled with fuzzy measures.Then,the practicability of the modeling method is demonstrated by the practical scenario.Secondly,based on fuzzy measure modeling,the importance and interaction of knowledge are discussed from the perspective of knowledge relevance.Finally,the application of knowledge relevance in cognitive diagnosis is studied.The influence of knowledge relevance on cognitive diagnosis is demonstrated through the experiments on real-world datasets.The results show that the proposed method predicts precisely with better interpretability.