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

Abstract

In modern education, personalized learning has garnered increasing attention, with cognitive diagnosis (CD) serving as a fundamental component. The objective of CD is to deeply comprehend students’ cognitive abilities, thereby identifying individual differences and providing support for personalized learning. However, many existing models ignore the influence of data noise and confidence in students’ responses. In addition, most existing methods equally extract evidence of students’ strengths and weaknesses from their overall responses, disregarding the characteristics of various types of relationships in students’ correct and incorrect responses. To this end, this study proposes a Confidence-guided Multigraph Model (CMM) for CD. First, to capture the changes and uncertainties in the student’s cognitive process, CMM dynamically calculates the confidence scores of the students’ responses. Second, CMM constructs multiple graphs to represent various relationships in students’ correct and incorrect responses. Then, CMM aggregates the unique information within the students’ correct and incorrect responses from various graphs. Finally, CMM uses a joint training strategy of multigraph iteration to simulate students’ cognitive ability in multiple dimensions. Extensive experimental results on two real-world datasets clearly demonstrate the efficacy of our proposed model. This innovative approach provides a novel perspective for the development of CD and lays the foundation for more accurate personalized learning support.