# Student Performance Prediction with Regression Approach and Data Generation
## 2 Related Works  
Educational databases are like gold mines to educators. To better understand the meaning of the data, or to find out the relationship among various factors, educators have put a lot of effort into trying out new methods to analyze the databases. The use of data mining techniques on educational databases to extract hidden information from the data \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Mathematics**} \\ \hline
**Methods** & **NV** & **NN** & **SVM** & **RF** \\ \hline Pass/Fail & 91.9\% & 88.3\% & 86.3\% & 91.2\% \\
5-level & 78.5\% & 60.3\% & 59.6\% & 72.4\% \\ \hline \hline  & \multicolumn{4}{c}{**Portuguese**} \\ \hline
**Methods** & **NV** & **NN** & **SVM** & **RF** \\ \hline Pass/Fail & 89.7\% & 90.7\% & 91.4\% & 92.6\% \\
5-level & 72.9\% & 65.1\% & 64.5\% & 73.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance reported by [PERSON] [6]. is called educational data mining. The idea of educational data mining emerged with the spread of online courses. The history of its development can be traced back to 1990s when the application of data mining to e-commerce proved to be a success [7]. As EDM is an interdisciplinary field, various methods are used by EDM researchers, including machine learning, statistics, psycho-pedagogy, information retrieval, cognitive psychology, and recommendation systems [8]. A study conducted by researchers in Singapore uses a new scoring function and model (Scoring Based on Associations [9]) to estimate a student's probability of failing and then use the association rule to select the recommended courses for the student [10]. The retention problem of students is mentioned in [PERSON]'s research [11]. DM techniques, including clustering, NN, and Decision Tree (DT), are used in the research to classify students, identify transfer candidates, and increase financial efficiency. [PERSON]'s study uses GritNet to predict student performance [12]. The study uses embedded student learning activity sequences as the input. By automatically processing these data with GritNet, this research attempts to shrink the feature dimensions of the data by avoiding the use of deficient data factors. From the results reported in this research, the GritNet is expected to have an advantage in \"creating a quickly adaptable but accurate metric to estimate long-term student outcomes\", which can speed up the feedback loop of student performance. A Recurrent Neural Network (RNN) is used on the Khan Academy Database to model student learning behavior in [PERSON]'s research [11]. Compared to non-neural network methods, RNN handles continuity better. Benefiting from RNN, the research is able to perform student modeling on sequential data. According to the results reported by [PERSON] [11], RNN shows a 25% improvement compared to Bayesian knowledge tracing using AUC (area under the curve) scoring. Among all the research on student performance prediction, a lot of researchers use classification algorithms. These researches are mostly limited to binary classification (fail/pass) [13; 14]. As the number of performance levels to be predicted increases, the performance of classification algorithms becomes worse. [PERSON]'s research [15] reports a prediction accuracy of around 52%, and [PERSON]'s research [16] reports an accuracy of around 65% for 5-level classification. Current educational data mining research is facing the following difficulties:  
Unlike some Artificial Intelligence (AI) research fields, current educational data mining does not have a widely used public dataset like CIFAR-10 in computer vision. Educational databases are highly varied. Educational databases from different sources can have very different features, components, and structures. In addition, sensitive information stored in student databases, such as grade, family educational level, parents' jobs, etc., makes student datasets accessible only to the institution that collected them.