# A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data
## 3 Proposed Method
### Scheme of Student Achievement Prediction  
The design scheme of the student achievement prediction model is discussed for details, as shown in Figure 1. This scheme is a general framework for student performance prediction. It is not limited to using the model proposed in this paper, other models can also be used. As can be seen in Figure 1, the scheme mainly consists of five parts, including datasets, data preprocessing, data extraction, data modeling and application. Next, each part in Figure 1 is described respectively. Datasets consist of raw data from databases, documents, or the website. Research on student achievement prediction has focused on education and psychology, using data mostly from questionnaires or student self-reports [29]. Generally, the acquisition of this kind of data should first understand data structure and meaning of the original student achievement data involved in the task, and determine the required data items and data extraction principles. Finally, the extraction of relevant student data is completed using appropriate means and strict operating specifications. The above process involves more relevant professional knowledge. We can try to combine the arguments of experts and users to obtain variables that are highly correlated with student performance. If the extraction of multi-source data is involved in the acquisition process, due to the different software and hardware platforms, it is necessary to pay attention to the connection of the data sources of these heterogeneous databases and the conversion of data formats. If the confidentiality of student data is involved, more attention should be paid to the operation of such relevant data during processing, and remarks should be made on the relevant data for reference. The study found that the possible reason why the prediction accuracy of the model could not be improved was caused by the quality of the data source. In the acquisition of raw data, it is particularly important to minimize errors and avoid mistakes from the source, especially to reduce human errors. Currently, the main sources of datasets on student achievement prediction are education management systems, offline datasets of educational history, and standardized test datasets. Data preprocessing needs to complete data cleaning, data integration, data transformation and other operations. In the whole data mining process, data preprocessing  
Figure 1: Student achievement prediction process scheme. takes about 60% of the time, and the subsequent mining work only accounts for about 10% of the total workload. The preprocessed data can not only save a lot of space and time, but also help the predictive model to make better decisions and predictions. Due to the different sources of educational data, the attributes and feature dimensions of student data are inconsistent. In order to obtain better quality modeling data, certain data cleaning, integration and transformation must be performed. Among them, data cleaning is the most time-consuming and tedious, but it is the most important step in the data preparation process. This step can effectively reduce the problem of conflict situations that may arise during the learning process. The raw data with conditions of noise, error, missing and redundant can be processed as follows. * Noise data. Data smoothing techniques are the most widely used methods for dealing with such noisy data [30]. * Error data. For some wrong data tuples, we change, delete or ignore these wrong data by analyzing the datasets. * Missing data. We use global constants or mean values of attributes to fill nulls, and use regression methods or use derivation-based Bayesian methods or decision trees to fix certain attributes of the data [31]. * Redundant data. We will remove redundant parts of the data to improve the processing speed of the prediction model. Data integration is a data storage technology and process that combines data from different data sources such as databases, networks, or public files.