Relationship Recognition between Knowledge and Ability Based on the Modularity of Complex Networks

The purpose of formal education is to increase students’ abilities, and its content is to impart knowledge through various courses. Thus, it is essential to accurately identify the relationship between knowledge and students’ ability increment to ensure the quality of education and the sustainable development of education. Currently, this relationship is mainly established based on previous educational data and teachers’ experience, which is often imprecise. This paper proposes a framework for knowledge and ability recognition based on the structural characteristics of complex network modules. The proposed framework utilizes a knowledge cognitive-interdependent network model (KCIN) as its object. First, the key knowledge nodes are identified via cognitive convergence flow of knowledge nodes in KCIN. Subsequently, the module structure of the knowledge network is identified by taking the key knowledge nodes as the core. Finally, the relationship between knowledge and ability is established by identifying the similar attributes of nodes in complex network modules. To validate the framework, we use teaching process data on the Data Structure course, which is a fundamental course for Information majors. The results show that the framework can effectively optimize the knowledge–ability relationship acquired from previous data and teacher experience.