# A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data
## 4 Experimental Studies
### Comparing the Results of All Experimental Algorithms
#### 4.2.3 Discussion  
We selected 6 experimental algorithms on the xAPI-Edu-Data dataset and the student achievement dataset. Compared with these experimental algorithms, the effectiveness of the proposed algorithm is verified. We observed the experimental results from Tables 3 and 4, we can easily find that the proposed algorithm has better classification accuracy, especially on the xAPI-Edu-Data dataset. However, in the classification of small sample data attributes, the prediction accuracy of the proposed algorithm is not significantly better than the comparison algorithms. In other words, the advantages of the proposed algorithm are more obvious on the attributes with more samples. Based on the above experimental results, it can be seen that the proposed algorithm effectively utilizes evolutionary membrane algorithm including objects, reaction rules and membrane structures, and is effective for training hyperparameters of spiking neural networks. The proposed model achieves good performance, but there are some shortcomings. First, the evolutionary spiking neural network method proposed in this paper can automatically learn  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Class** & **Precision** & **Recall** & **F1-Score** & **Support** & **Accuracy** \\ \hline \multirow{6}{*}{Logistic Regression} & A & 0.60 & 0.75 & 0.67 & 4 & \\  & B & 0.75 & 0.64 & 0.69 & 14 & \\  & C & 0.33 & 0.62 & 0.43 & 8 & 0.7215189873417721 \\  & D & 0.69 & 0.43 & 0.53 & 21 & \\  & E & 0.91 & 0.97 & 0.94 & 32 & \\ \hline \multirow{6}{*}{Decision Tree} & A & 0.75 & 0.75 & 0.75 & 4 & \\  & B & 0.90 & 0.64 & 0.75 & 14 & \\  & C & 0.38 & 0.75 & 0.50 & 8 & 0.7468354430379747 \\  & D & 0.71 & 0.57 & 0.63 & 21 & \\  & E & 0.91 & 0.91 & 0.91 & 32 & \\ \hline \multirow{6}{*}{XGBoost} & A & 1.00 & 0.25 & 0.40 & 4 & \\  & B & 0.80 & 0.86 & 0.83 & 14 & \\  & C & 0.38 & 0.38 & 0.38 & 8 & 0.7721518987341772 \\  & D & 0.72 & 0.62 & 0.67 & 21 & \\  & E & 0.86 & 1.00 & 0.93 & 32 & \\ \hline \multirow{6}{*}{AdaBoost} & A & 0.00 & 0.00 & 0.00 & 4 & \\  & B & 0.75 & 0.86 & 0.80 & 14 & \\  & C & 0.60 & 0.75 & 0.67 & 8 & 0.46835443037974683 \\ \cline{1-1}  & D & 0.36 & 0.90 & 0.51 & 21 & \\ \cline{1-1}  & E & 0.00 & 0.00 & 0.00 & 32 & \\ \hline \multirow{6}{*}{Neural Network} & A & 0.60 & 0.75 & 0.67 & 4 & \\  & B & 0.73 & 0.57 & 0.64 & 14 & \\ \cline{1-1}  & C & 0.33 & 0.50 & 0.40 & 8 & \\ \cline{1-1}  & C & 0.33 & 0.50 & 0.40 & 8 & \\ \cline{1-1}  & D & 0.75 & 0.57 & 0.65 & 21 & \\ \cline{1-1}  & E & 0.91 & 0.97 & 0.94 & 32 & \\ \hline \multirow{6}{*}{Proposed Algorithm} & A & 1.00 & 0.75 & 0.86 & 4 & \\ \cline{1-1}  & B & 0.92 & 0.86 & 0.89 & 14 & \\ \cline{1-1}  & C & 0.50 & 0.75 & 0.60 & 8 & \\ \cline{1-1}  & D & 0.83 & 0.71 & 0.77 & 21 & \\ \cline{1-1}  & E & 0.94 & 0.97 & 0.95 & 32 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparing results with all experimental algorithms on the student performance dataset.  
effective representations of samples and achieve good model performance, but it is slightly insufficient in model interpretability. Second, this paper only uses the standard student achievement dataset, and only uses the basic attribute information and grades of students. The content of the course knowledge points and compulsory courses will not be explored and utilized. Comprehensive use of multivariate data related to student performance to train models will facilitate in-depth research on evolutionary spiking neural networks.  
To sum up, centering on the research topic of student achievement prediction, this paper conducts a more in-depth and systematic research on student achievement prediction from the perspective of student achievement, and the focus of the work is to improve the predictability and accuracy of the proposed model for predicting student achievement based on an evolutionary spiking neural network.