# NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features
## 2 Related Work  
In smart education, cognitive diagnosis of student status is a vital and fundamental task in realistic scenarios, such as personalized student tutoring [16], early warning of academic status [17], and student preference prediction [18]. For this reason, some classical cognitive diagnostic models have been proposed to solve such problems [19]. For example, the IRT model is one of the most classic models that models students' diagnoses through their answer records, and characteristics such as the difficulty of the exercises. Multidimensional item response theory models (MIRT) proposed by [PERSON] et al. [20] is a type of modeling of competencies, item characteristics, and diagnostic performance. It transforms the previous unidimensional cognitive diagnostic model into a multidimensional cognitive diagnostic model. Although these cognitive diagnostic models can diagnose the student'sknowledge state, the description of the student's state is a one-dimensional competency value. The potential representation of student states by ability values makes the models poorly represented in terms of interpretability. In order to enhance the understandability of the model, the DINA was proposed by [PERSON] et al. [9], who utilized students' exercises records and the exercise knowledge concept association matrix (Q-matrix) to describe the students as a multidimensional vector of competencies on the knowledge points, so that the student competency vector corresponds to the knowledge concepts. However, the DINA also has some shortcomings. In response to the problems of the DINA model, researchers have carried out extensive research. For example, fuzzy cognitive diagnosis for modeling examinee performance (FuzzyCDF) was proposed by [PERSON] et al. [21], who addressed the situation that the traditional DINA only diagnoses objective questions and does not consider the diagnosis of subjective questions. Student ability was represented as the affiliation of a fuzzy set (a real number in the range of the set [0, 1]) and two cognitive diagnosis models, fuzzy intersection and fuzzy concatenation, were used to diagnose objective and subjective questions separately. Higher-order latent trait models for cognitive diagnosis (HO-DINA) were proposed by [PERSON] and [PERSON] [22] considered that students' knowledge acquisition is influenced by one or more higher-order features of the exercises. Therefore, the model combines a higher-order latent structure model and DINA to specify the joint distribution of binary attributes in the cognitive diagnostic model through higher-order latent features, improving the utilization of less informative, but cognitively specific, data. In addition, there are also DINA-based cognitive diagnostic methods, such as GDINA [23], GDI [24], ESVE-DINA [25] and other cognitive diagnostics [26]. All these methods further enhance the diagnostic capability of DINA. However, the use of information from the exercises is still limited by the manually designed functions, which make it difficult to adequately deal with the nonlinear relationship between students and the exercises. With the rapid development of deep learning, the powerful data processing ability of neural networks has achieved great success in all major fields [27; 28], and the introduction of neural networks into cognitive diagnosis models has become a hot research topic [29]. Deep learning enhanced item response theory for cognitive diagnosis (DIRT) was proposed by [PERSON] et al. [30], who used neural networks to enhance the extraction of textual information from exercises and improved the application of the model to sparse data. The NeuralCDM [14] proposed the use of neural networks instead of traditional logical functions to model the interaction between students and exercises. However, the processing of interaction information is not comprehensive enough and lacks the modeling of some of the exercise features (e.g., guess and slip factor) and the integration of multiple exercise features.