# A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data
## 1 Introduction
introduced an adaptive recommendation system to predict student's academic performance in the faculty of engineering at AL-Azhar University, which can recommend the best machine learning algorithm for each faculty department to predict student's academic performance [12]. [PERSON] et al. proposed some theoretical concepts about the support vector regression machine learning method with a focus on improving the computational efficiency of their national high school exams for online test [13]. [PERSON] et al. implemented a Bidirectional Long Short-Term Memory (BiLSTM) network based on improved feature selection to efficiently predict student performance from historical data. The simulation results show that the proposed method has achieved a prediction accuracy of 90.16% [14]. The above experiences are very meaningful for improving the prediction accuracy of student achievement. These models have been successfully applied to student performance prediction, but there are still some deficiencies in the training and design stages of the prediction model. For example, the law analysis of the educational data itself is insufficient, and the prediction accuracy of the model needs to be further improved, and so on [1; 16; 15]. Aiming at the above problems, this paper mainly explores a student achievement prediction model based on spiking neural network. Based on our previous work [17; 18], this paper aims to study learning prediction models based on spiking neural networks to further improve the accuracy of student achievement prediction. Subsequently, the proposed model was applied to the two student grade prediction datasets of Alibaba Cloud Tianchi's xAPI-Edu-Data and UCI machine learning library. The proposed prediction model is compared with state-of-the-art models to verify its effectiveness. The experimental results show that the proposed model outperforms these experimental models in classification accuracy. The main contributions of this paper can be summarized as follows. * Based on the analysis of educational data, an educational data mining model is discussed. * Spiking neural network is used for the first time to predict student achievement. * Evolutionary spiking neural network is designed and implemented on the basis of the student datasets. * Simulation results verify the effectiveness of the proposed model in predicting student achievement. * The research results of the proposed model can provide more targeted reference for scientific research and education management workers. The remainder of this paper is summarized as follows. Section 2 discusses the current research state in educational data mining and spiking neural networks. Section 3 describes the proposed model in detail, especially how to design educational data mining models based on evolving spiking neural networks. In Section 4, the proposed model is compared with the state-of-the-art models, and the simulation results are evaluated and discussed on the xAPI-Edu-Data and student-grade-prediction datasets, respectively. Finally, Section 5 summarizes the conclusions of this paper.