Constructing a Confidence-guided Multigraph Model for cognitive diagnosis in personalized learning

Yu Su, Ze Han, Shuanghong Shen, Xuejie Yang, Zhenya Huang, Jinze Wu, Huawei Zhou, Qi Liu

Abstract: Highlights•We considered confidence information to reduce the impact of data noise and sparsity.•A model using multi-graph to represent different types of relationships is developed.•We design a joint training strategy to integrate multiple levels of information.•Our model achieves more precise and robust results in diagnosis results.