# Student Performance Prediction with Regression Approach and Data Generation
## 3 Experiment and Analysis of Results
### Generation and Use of Synthetic Data  
According to Tables 1 and 3, it is evident that NNs perform less effectively on the mathematics dataset compared to the Portuguese dataset, regardless of whether the regression or classification approach is used. With limited artificial design in their structures, simple fully connected NNs are very flexible and, thus, can perform many tasks well. Less artificial design also makes NNs prefer large training datasets; since everything in an NN has to be learned from the very start, the more data, the clearer the knowledge, and the better the learning result. Given that the student mathematics performance dataset used in this research is smaller than the Portuguese dataset, it is possible that the lack of student mathematics performance data causes NNs to perform worse on the mathematics dataset. Although the best way to improve the performance of NNs on small datasets is to collect more data, it is still unavoidable for researchers to encounter conditions where data volume is limited. Considering the differences between schools, regions, teacher skills, etc., it is difficult to combine student data from different schools. Furthermore, due to privacy concerns, many students may not agree with the use of their personal information for educational data mining. Improving prediction performance on small datasets is still meaningful. AI-Generated Content (AIGC) has recently become a hot topic, while research into generative AI has been underway for a long time. From 2014 on, [PERSON] has been conducting research using two adversarial networks to generate images [18] and named the networks Generative Adversarial Networks (GANs). After the success of GANs, some researchers used AI-generated images to improve the performance of models on small datasets [19; 20]. In this research, GANs are chosen to generate student performance data to augment the student mathematics dataset. The student data used in this research contain 33 features, each of which has a different value domain. Some of the features use text values (such as school names, gender, etc.). This makes the student data highly discrete. A common method of dealing with text data is to convert it to 0's and 1's using one-hot encoding. However, this makes the number of features in the data much larger--there will be 59 features after one-hot encoding, and most data in the features created by one-hot encoding is 0, which contains very little information. As reported in Table 4, GANs trained with one-hot encoded data cannot properly generate data that cover the data distribution of the original data. The sampled data are very similar--their ages, Medu, and Fedu values are all the same. Furthermore, their grades G1 and G2 are also too low and hardly appear in the collected dataset. Considering that the original outputs of GANs are re-scaled into the value domain by post-processing (e.g., age smaller than 15 will be rescaled into 15 since the age domain is 15-22), it is possible that the original outputs of the GANs are much worse. The experiment result shows that one-hot \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mathematics**} & \multicolumn{3}{c}{**Portuguese**} \\ \hline  & **SVR** & **NN** & **RF** & **SVR** & **NN** & **RF** \\ \hline Regression & 81.5\% & 73.4\% & 78.2\% & 75.9\% & 79.5\% & 72.8\% \\ Classification & 60.3\% & 59.6\% & 72.4\% & 65.1\% & 64.5\% & 73.5\% \\ Improvement 1  & +21.9\% & +13.1\% & +5.8\% & +11.4\% & +14.4\% & \(-\)0.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction accuracy with the regression approach. encoded data are not compatible with GANs. More steps are needed for the GANs to work properly. An encoder and a decoder are added to help the GANs. The encoder and decoder used in this research are trained as a three-layer neural network--an input layer of 59 dimensions, a hidden layer of 15 dimensions, and an output layer of 59 dimensions.