Continuous Weighted Neural Cognitive Diagnosis Method for Online Education

With the rapid development of online education, extensive data records from online education are accumulated in large quantities, therefore the educational evaluation industry is of great potential. Cognitive diagnosis based on machine learning has drawn considerable attention from both the research community and industry, and a lot of works have been proposed. However, many models ignored the point that different knowledge concepts have different important degrees on each exercise. In this paper, we propose the Continuous Weighted Neural Cognitive Diagnosis (CWNCD) model, which is extended from the Neural Cognitive Diagnosis (NCD) framework, a cognitive diagnosis framework based on neural network, to get a more accurate diagnosis result and ensure its interpretability. Specifically, we added information about the importance degree of different knowledge concepts in each exercise for modeling their interactions, in which case we can more comprehensively model the cognitive level of a student. Extensive experiments conducted on real-world datasets show that the CWNCD model is feasible and obtain excellent performance. Finally, the possible future research directions are discussed.