# NeuralNCD: A Neural Network Cognitive Diagnosis Model Based on Multi-Dimensional Features
## 1 Introduction  
In recent years, with the fast proliferation of big data in education, intelligent education systems [1] and large-scale online education platforms [2] have become more and more widely accepted. Smart education systems and online education platforms enable learners to learn what they need faster and easier. However, the large amount of educational resources and the difference in the knowledge level of each learner make it a challenge for learners to learn what they need in a limited time [3]. The ways in which one can quickly and accurately understand learners' knowledge levels, help learners learn the concepts they have not yet mastered, or consolidate the concepts they have mastered have become an important research topic in the current field of intelligent education [4, 5]. For this reason, scholars have introduced cognitive diagnostic models to track and diagnose students' knowledge states [6]. Recently, some scholars have put significant effort and extensive work into the subject of cognitive diagnostics, and it has resulted in significant progress [7]. For example, item response theory (IRT) [8] modeled students into one-dimensional ability values by combining exercise characteristics, such as difficulty, discrimination, and students' knowledge states. Deterministic input, noisy \"And\" gate model (DINA) [9] combined the Q-matrix and the student's existing answer records to model the student as a multidimensional ability vector on the knowledge points. However, the interaction functions used in the above models still have some shortcomings in dealing with the relationship between students and exercises. For example, the logistic function [10] and the inner product [11]. With the rapid development of deep learning algorithms, the powerful processing of information by artificial neural networks has been noticed by scholars. For example, neural networks have been used to simulate energy conversion [12] and pedestrian detection [13], and good results have been obtained for both. In the field of cognitive diagnostics, neural cognitive diagnosis for intelligent education systems (NeuralCDM) was proposed by [PERSON] et al. [14] and used neural networks to model the interaction between students and exercises, which enhances the use of information. However, these models still have some shortcomings. For example, there is a lack of exploration of integrated modeling of multiple exercise features and the further processing of the nonlinear connection between students and exercises that neural networks can provide. To this end, this paper proposes a neural network cognitive diagnosis model based on multi-dimensional features, which is based on the NeuralCDM and uses a 3-parameter logistic model [8] to enable the incorporation of multiple exercise features into the cognitive diagnosis model. The complex interaction of students and exercise is modeled by a neural network. In addition, data preprocessing is performed on the input of each layer of the neural network [15], so that the neural network can better capture the connection between students and exercises and improve the accuracy of the model. Finally, the monotonicity assumption is applied to the neural network to enhance the interpretability of the model. The main contributions in this paper are summarized as follows:  
(1) In this paper, we propose a neural network cognitive diagnosis model based on multi-dimensional features. Based on a 3-parameter logistic model, the model can effectively utilize multiple exercise features (e.g., difficulty, discrimination, guess and slip factor), so that it can better diagnose students' knowledge states. (2) The model introduces data preprocessing mechanisms and monotonicity assumptions to enhance the accuracy and interpretability of the model. A data batching layer is added to the neural network to enable it to better capture the interaction information between students and exercises. The monotonicity assumption is introduced to make the diagnosed student's states more interpretable.