# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 4 Discussion  
The accuracy of models in classifying student skill mastery is crucial in CDAs and directly determines the reliability of the model. In the past, researchers have proposed many parametric and nonparametric CDMs, drawing from the fields of statistics and vector distance. Despite these efforts, a universally robust method adaptable to various conditions remains elusive. In recent years, the rapid advancements in artificial intelligence (AI), particularly in fields like natural language processing (NLP), have highlighted the potential of ANNs [32]. As a powerful machine learning method, ANN application in CDAs has not received substantial attention, although some researchers have begun exploring this avenue. In response to this gap, in this work, we proposed Dual Q-Net, a novel neural-network-based cognitive diagnostic method. This method, inspired by the GDINA model, leverages Q-matrix and interactive Q-matrix constraints and is rooted in deep cognitive diagnosis principles. The previous sections detailed the methodology and evaluated its performance extensively. In the following sections, we will delve into various aspects, including the design principles and interpretability of Dual Q-Net. Firstly, we will talk about the architecture and interpretability of the Dual Q-Net model. The design of our method was inspired by the GDINA model. The contribution of GDINA to the correct identification of skill patterns consists of three parts: guesses and slips, direct effects of skills, and interaction effects between skills [7]. Similarly, our proposed method, Dual Q-Net also contains three parts, namely the bias parameters representing guess and slip in each layer of the network, the orange computation flow in Figure 2 that represents the main effects, and the green computation flow that represents the interaction effects among skills. In addition, we also referred to the DINA model to design a very simple and easy-to-understand interactive Q-matrix [33], and applied it together with the Q-matrix to our network structure to achieve constraints on the network structure. This is the most prominent difference between our method and the methods of [11; 24]. Their network structure was fixed, thus making it difficult to adapt their method to different data. In contrast, our design based on this approach allows the network structure of Dual Q-Net to be dynamically generated, and the network structure can be dynamically adapted for different datasets based on different Q-matrices and interactive Q-matrices. Moreover, our method employs an interactive Q-matrix to constrain the weights of the hidden layer network, ensuring that our method avoids overfitting. The findings of numerous previous studies and the results of traditional methods in this study have consistently indicated that as the attribute structure becomes more complex, the classification accuracy decreases. Moreover, higher sliding and guessing values are associated with a lower classification accuracy, while an increase in the number of training samples correlates with a higher classification accuracy [25]. These design features, which ensure that our method is more robust to the number of attributes and still performs well on data with high guesses and slips, mean that our method does not strictly adhere to the above laws on both simulated and real datasets. Secondly, in terms of model training, [PERSON] et al. trained their networks using ideal response patterns and ideal mastery patterns [11], and [PERSON] et al. trained their network parameters based on a semi-supervised method of co-training [15]. For a given Q-matrix, the ideal data are very limited and do not cover the real responses of the students [34]. As a result, neural network models trained on the basis of ideal data suffer from poor generalization and performance compared to traditional CDMs on a test set.