# Student Performance Prediction with Regression Approach and Data Generation
###### Abstract  
Although the modern education system is highly developed, educators have never stopped looking for new ways to improve it. After entering the 21 st century, more and more educational data are stored, and data mining techniques have developed rapidly. Educational data mining has become a hot topic for educators who want to discover the information hiding among educational data. As a sub-branch of educational data mining, student performance prediction aims to predict student performance based on student datasets. This research attempts to improve the performance of predictive algorithms on a 5-level student performance grading system. This research changes the prediction method from a classification approach to a regression approach and enlarges small datasets with synthetic data. Algorithms including Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN), and Generative Adversarial Networks (GANs) are used in this research. From the results obtained, it is concluded that the regression approach outperforms the classification approach in predicting student performance. The classification approach is currently widely used in student performance prediction. This research also explores the possibility of using synthetic student data to augment small educational datasets. The course and evaluation system differ among different regions, making student data hard to collect or merge. Augmenting small student datasets with synthetic data may help educators to better evaluate their teaching skills. This research shows that a regression approach using synthetic data improves the prediction accuracy by up to 21.9%, 15.6%, and 6.6%, respectively, using SVM, NN, and RF. educational data mining student performance prediction; generative adversarial networks +
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models data to find out implicit but useful information behind the data [3; 4; 5]. Unlike traditional data analysis that focuses on using statistical methods, DM combines computational methods like Machine Learning (ML) to discover the information that might be ignored or go unnoticed by traditional methods. DM has been successful in various fields, including business and economics. A huge amount of educational data is now stored by both offline educational institutions and online course providers, which is like a gold mine to DM. Discovering useful information hidden in educational data could greatly help teachers better understand their students and their teaching process. The application of data mining techniques to educational databases is called Educational Data Mining (EDM). Student performance prediction is a branch of EDM that predicts student performance based on collected information (family educational background, health condition, time spent on coursework, previous performance, etc.). By predicting student performance, teachers can adjust their teaching plan in time to avoid unnecessary failures and make plans according to each student's condition. In practice, although there is a huge amount of educational data stored all over the world, it is still hard for educators to obtain access to large educational datasets. Different schools have different course settings, making it almost impossible to merge datasets from different schools. Furthermore, because student datasets contain sensitive information, they are usually not accessible to outsiders. In this situation, the performance of DM techniques that prefer large datasets, such as NNs, is limited. In 2008, [PERSON] from the University of Minho carried out a study using DM techniques to predict the performance of secondary school students in Portuguese and Mathematics [6]. As stated in the research, Portugal was facing a high student failure and drop-out rate at that time. This issue is also a concern for many other countries facing a shortage of educational resources. In addition, countries that do not use English as a primary language may also face the difficulty of learning from English-based course settings.