# A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data
## 4 Experimental Studies
### Comparing the Results of All Experimental Algorithms
#### 4.2.2 Comparing Results with All Experimental Algorithms on the Student Performance Datasets  
In this section, the student performance dataset from the UCI machine learning repository is selected as the experimental data to further validate the advantages of the proposed model in comparison with other experimental models.  
First, we still analyze the relationship between student achievement and gender. As can be seen in Figure 9, it is clear that the grades of male and female are very similar, but the failing number of female is higher than male. The result of Figure 9 suggests that gender has little effect on final grades in the student performance dataset.  
Next, heatmap is utilized to further analyze the interaction between different attributes of students, as shown in Figure 10. It is not difficult to find from Figure 10 that the influence between these attributes is large, such as'school', 'age', 'famsize', 'Medu', 'Mjob','reason', 'traveltime', 'failures', 'famsup', 'activities', 'higher', 'romantic', 'freetime', 'Dalc', 'health', 'abscences', 'G1', 'G2'. Among these student attributes, 'G1' and 'G2' are the most important ones that directly affect the final grade.  
Figure 10: Heatmap of student features attributes  
Figure 9: The relationship between student achievement and gender.  
Next, the feature importance curve is shown in Figure 11. The target attribute 'G3' has a strong correlation with the attributes 'G1' and 'G2'. This is because 'G3' is the final year grade (issued in the third semester), while 'G1' and 'G2' correspond to the first and second semester grades. In Figure 11, it is difficult to predict 'G3' without 'G1' and 'G2'. However, it is possible to successfully predict 'G3' in the absence of 'G1' and 'G2' in the student performance dataset if more important data features can be found from other datasets.  
Observing the above experimental results from Figures 9-11, it can be concluded that there is a strong correlation between 'G1', 'G2' and students' final grades. Therefore, student achievement is related to attributes such as'school', 'age', 'famsize', 'Medu', 'Mjob','reason', 'traveltime', 'failures', 'famsup', 'activities', 'higher', 'romantic', 'freetime', 'Dalc', 'health', 'abscenes'.  
On this basis, the proposed and comparative models are executed on the dataset and evaluated using three metrics. Table 4 describes the comparison results with all experimental algorithms on the student performance dataset. 'A', 'B', 'C', 'D' and 'E' in Table 1 stand for excellent/very good, good, satisfactory, pass and fail.  
To verify the advantages of the proposed algorithm, the proposed algorithm is conducted on the student performance dataset and compare the quantitative results with other experimental algorithms in Table 4.  
In term of Precision, XGBoost, SVM and the proposed algorithm have the same and best results on 'A', and SVM and the proposed algorithm achieve the best results of 0.92 on 'B', and AdaBoost get the best results on 'C', and the proposed algorithm is better than on other experimental algorithms on 'D', and 'E'. For the second indicator Recall, SVM obtains the best results on 'A', and XGBoost, AdaBoost, SVM, and the proposed algorithm have the same and best result on 'B', and Decision trees, AdaBoost and the proposed algorithm achieve the best results on 'C', and AdaBoost on 'D' have the best result, and XGBoost have the best result on 'E'. The last indicator F1-score, the result of SVM is 1 on 'A' which means the best result compared with all experimental algorithms, and SVM and the proposed algorithm have the same results on 'B' that is better than other experimental algorithms, and AdaBoost on 'C' have the best result, and the proposed algorithm have the best result on 'D' and 'E'. Finally, the accuracy achieved by the proposed algorithm is 0.814, which is better than all experimental algorithms.  
Figure 11: Feature importance comparison of student datasets.