# Student Performance Prediction with Regression Approach and Data Generation
## 4 Conclusions and Future Work  
Following the concept of applying data mining techniques to educational data and the dataset contributed by [PERSON] [6], this research is able to achieve better results in predicting student performance. Based on the experiment result, it is evident that the regression approach has an advantage over the classification approach in predicting student performance. Improvements of 21.9%, 13.1%, and 5.8% are achieved with SVR, NN, and RF in predicting student mathematics grades, respectively. Improvements of 11.4% and 14.4% are achieved with SVR and NN in Portuguese, respectively. Switching from a classification approach to a regression approach significantly improves the accuracy of predicting student performance on the 5-level grading scale. The improvement in predictive accuracy on the multi-level grading system is expected to help educators better understand their students.  
This research also explores the possibility of using AI-generated data to enhance the prediction performance on small datasets. The slight improvement of 2.5% achieved with NN shows the potential of employing synthetic data in educational data mining. The experiment on data generation also shows the difficulties of training GANs on small datasets with discrete data distribution and inconsistent data features. In conclusion, finding a method that efficiently projects collected data into a more GAN-friendly hyperspace could greatly improve the quality of generated data. To further improve the prediction performance, the quality of the synthetic data needs to be improved. In future studies, the search for a better projecting method should aid the training of GANs. Further, noise should be added to GANs to control the network and generate more dispersed data. In addition, methods that assess the quality of the generated data are preferred.  
Overall, this research highlights the effectiveness of a regression approach over a classification approach in predicting student performance. Additionally, this research demonstrates the feasibility of using generated data to augment small educational datasets. As technology continues to develop, a variety of teaching methods, such as online courses, are available to the public. For educators, efficient use of modern techniques can greatly enhance their understanding of their students. Artificial Intelligence is currently a tool with unlimited potential. Applying artificial intelligence methods to educational datasets enables educators to adjust their teaching strategy according to students' conditions in real time. Making more personalized teaching plans for each student to help them achieve greater success may one day become possible with the help of AI technology.  
Conceptualization, D.Y. and J.M.; methodology, D.Y.; software, D.Y.; validation, D.Y. and J.M.; formal analysis, D.Y.; investigation, D.Y.; resources, D.Y.; data curation, D.Y.; writing--original draft preparation, D.Y.; writing--review and editing, D.Y.; visualization, D.Y.; supervision, J.M.; project administration, D.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.