# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
); [EMAIL_ADDRESS] (N.C.)  
[PERSON] 1  
1 School of Information Science and Technology, Northeast Normal University, 25555 Jingyue, Changchun 130117, China; [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (N.G. ); [EMAIL_ADDRESS] (N.C.); [EMAIL_ADDRESS] (Q.G. ); [EMAIL_ADDRESS] (N.C.)  
[PERSON] 1  
1 School of Information Science and Technology, Northeast Normal University, 2555 Jingyue, Changchun 130117, China; [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (N.G. ); [EMAIL_ADDRESS] (N.C.); [EMAIL_ADDRESS] (Q.G. ); [EMAIL_ADDRESS] (N.C.)  
[PERSON] 1  
1 School of Information Science and Technology, Northeast Normal University, 25555 Jingyue, Changchun 130117, China; [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (N.G. ); [EMAIL_ADDRESS] (N.C.); [EMAIL_ADDRESS] (Q.G. ); [EMAIL_ADDRESS] (N.C.)  
[PERSON] 1  
1 School of Information Science and Technology, Northeast Normal University, 25555 Jingyue, Changchun 130117, China; [EMAIL_ADDRESS] (J.T. ); [EMAIL_ADDRESS] (N.G. ); [EMAIL_ADDRESS] (N.C.); [EMAIL_ADDRESS] (Q.G. ); [EMAIL_ADDRESS] (N.  
the non-parametric classification model based on vector similarity calculation (e.g., NPC and GNPC [5, 6]). However, the most studied methods are the parametric methods based on statistical theory, such as the saturated models (e.g., LCDM and GDINA [7]) and the reduced models (e.g., DINA, RRUM, ACDM, and DINMix [8, 9]). In recent years, with the rapid development of artificial intelligence technology, some researchers have tried to use artificial neural networks (ANNs) for cognitive diagnosis [10]. [PERSON] et al. synthesized ideal responses to train a multilayer perceptron (MLP) and evaluated its performance in the DINA framework [11]. [PERSON] et al. combined an artificial neural network (ANN) and a hidden Markov model (HMM) to monitor students' cognitive skill development [12]. Their method can effectively track students' cognitive skill development, but the accuracy is affected by item quality, item quantity, and the skills examined. [PERSON] et al. proposed a neural cognitive diagnosis framework called NeuralCD for student cognitive diagnosis in intelligent education systems [13]. The framework uses neural networks to simulate the nonlinear interaction between students and exercises, and it has a certain interpretability. [PERSON] et al. studied the impact of the complexity of the skills or knowledge concepts being examined on the accuracy of artificial neural networks in cognitive diagnosis [14]. Their results showed that the higher the complexity of the knowledge-skill structure, the lower the classification accuracy. [PERSON] et al. integrated ANN with DINA and DINO models to achieve a semi-supervised neural network cognitive diagnosis model [15]. Their results showed that when the discrimination of items decreased, the diagnostic accuracy of their method decreased, but their model had good robustness to noise. In summary, numerous researchers have applied neural networks to cognitive diagnosis in recent years. However, it can be observed that almost none of these studies addressed how to determine a network structure for cognitive diagnosis, such as the depth of the network and the number of neurons in each layer, for different testing scenarios. To address this issue, we propose a Q-matrix-constrained neural network design method for cognitive diagnosis. Specifically, our method is highly consistent with the GDINA model [7], and the network structure reflects the direct contribution of skill or knowledge  
Figure 1: Example of a cognitive diagnostic process, used to infer and predict students’ level of mastery of knowledge concepts through cognitive diagnostic assessments. (**a**) Collection of student response data: The Q-matrix is transformed into test items, with student responses recorded. Circles in different colors represent distinct knowledge concepts or skills, and items denote test questions composed of these concepts. Solid connecting lines indicate the constituent relationship between items and the respective knowledge concepts or skills.