A novel cognitive diagnosis system with attention residual mechanism and broad learning

Jing Wang, Jialin Miao, Junwei Duan, Xiping Jia, Zhiyong Lin

Abstract: As machine learning and deep learning gain increasing prominence across diverse domains, the realms of intelligent education and computer-assisted educational technologies are constantly converging, facilitating the development of innovative pedagogical approaches. Simultaneously, with the escalating emphasis on personalized education, traditional test scores alone are no longer sufficient to comprehensively reflect students’ learning status and progress. Cognitive diagnosis aims to uncover the latent cognitive information and skills underlying students’ performance scores. By tapping into the educational data hidden beyond scores, it is possible to gain a deeper and more nuanced understanding of students’ learning profiles and abilities. Consequently, this paper proposes a novel cognitive diagnosis system based on Broad Learning System (BLS). Within this system, an attention residual block is designed to efficiently extract information from interrelated modules, taking into full consideration their interdependencies and correlations. To validate and assess the accuracy of the extracted diagnostic information as well as the rationality and effectiveness of the proposed model, student performance is predicted utilizing the Broad Learning System. Experimental results demonstrate that the proposed system exhibits excellent performance in both the extraction of latent cognitive information and the prediction of student performance.