# A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data
## 3 Proposed Method
### Student Achievement Prediction Model  
Figure 2 shows a student achievement prediction model based on an evolutionary spiking neural network. First, the processed modeling data is divided into a training dataset and a test dataset according to a certain proportion. Next, the evolutionary spiking neural network model is proposed for the student achievement prediction. Then, the proposed model is trained using the training dataset, and the evolutionary membrane algorithm is used to optimize the hyperparameters of the proposed model to obtain the best output performance. A test dataset is chosen to evaluate the performance of the proposed model. On the test dataset, the prediction effect of the proposed model is analyzed by some evaluation indicators. As can be seen in Figure 2, the working mode of the spiking neural network proposed in this paper can be roughly divided into some parts, including the encoding of the input data, the training and learning of the spiking neural network, and the decoding and output of the predicting results. Each part of the proposed is discussed as follows. * We take the student dataset as the input of the proposed model, and encode these data as the input spike sequence using the first spike encoding [32]. * The input spike sequence is passed to neurons for transmission and processing, and then the learning rate and synaptic time delay are optimized using evolutionary membrane algorithm to achieve adaptive tuning the hyperparameters of the proposed model. Figure 2: Architecture of Student Achievement Prediction Model. * The processing result of the neuron is passed to the output layer. The output layer outputs the predicted spike sequence, and calculates the mean squared error between the actual spike sequence and the expected output spike sequence. * The model adjusts the learning rate and synaptic time delay by continuously calling the evolutionary membrane algorithm to reduce its prediction error value. Until the mean squared error is smaller than a certain limit or the number of iterations satisfies the requirement of stop learning, the proposed model outputs a spike sequence and decodes this sequence into a prediction result. For a deeper understanding of the proposed model, a flowchart of the proposed student achievement model based on evolutionary spiking neural network is shown in Figure 3.  
Figure 3 specifically shows the working process of the entire model. This model first determines the characteristic data that affects student performance as the input sequence of  
Figure 3: Flowchart of Student Achievement Prediction Model. the spiking neural network, and uses the first coding method to convert these data into input spiking sequences and expected spiking sequences. On this basis, the model passes the input spiking sequence to the neuron of the spiking neural network, and the data is learned and trained in the neuron. Then, the final data learned by the neuron is passed to the output layer, and the meaning square error between the actual spiking sequences and the expected spiking sequences is calculated. Next, it is necessary to compare the error value with the error standard or determine whether the number of iterations meets the ending requirements. The evolutionary membrane algorithm is used to adjust the learning rate and synaptic time delay of the neuron, and this process is executed cyclically until the error value meets the requirements, or the number of iterations satisfies the ending condition. The evolutionary membrane algorithm used in this paper consists of objects, membrane structures, and reaction rules. The object represents the hyperparameters of the spiking neural network. The membrane structure is a two-layer structure composed of a skin membrane and several membranes. The chemical reaction optimization algorithm as reaction rules evolve these objects in the membrane. Then, evolutionary membrane algorithm evaluate each object in the membrane, and compare the fitness values of these objects.