Predicting Math Performance in High School Students using Machine Learning Techniques

In the field of education, understanding and predicting student performance plays a crucial role in improving the quality of system management decisions. In this study, the power of various machine learning techniques to learn the complicated task of predicting students' performance in math courses using demographic data of 395 students was investigated. Predicting students' performance through demographic information makes it possible to predict their performance before the start of the course. Filtered and wrapper feature selection methods were used to find 10 important features in predicting students' final math grades. Then, all the features of the data set as well as the 10 selected features of each of the feature selection methods were used as input for the regression analysis with the Adaboost model. Finally, the prediction performance of each of these feature sets in predicting students' math grades was evaluated using criteria such as Pearson's correlation coefficient and mean squared error. The best result was obtained from feature selection by the LASSO method. After the LASSO method for feature selection, the Extra Tree and Gradient Boosting Machine methods respectively had the best prediction of the final math grade. The present study showed that the LASSO feature selection technique integrated with regression analysis with the Adaboost model is a suitable data mining framework for predicting students' mathematical performance.