Predicting Students Answers Using Data Science: An Experimental Study with Machine Learning

Abstract

In today’s data-driven world, the abundance of information provides us with opportunities to explore the relationships between various data points, leading to progress in multiple domains. For instance, in the field of education, we can leverage students’ past course performance and academic records to offer tailored guidance, allowing them to concentrate their efforts on specific areas for academic growth. By employing machine learning techniques, we can analyze data relations and predict future events based on historical data. In this study, we utilized machine learning techniques on the educational dataset from NeurIPS 2020. We aimed to improve the prediction of upcoming student performance by adding valuable features. To accomplish this, we explored several classification algorithms, including SVM, Naive Bayes, Logistic Regression, and Decision Tree. Additionally, we considered Ensemble methods such as Boosting, Bagging, and Voting. By assessing the optimal hyperparameter values for these algorithms, we aimed to optimize their performance. Our findings revealed that augmenting the dataset with more correlated features significantly improved prediction accuracy. Among the classifiers examined, Decision Tree, XG Boost, and Voting exhibited the best performance, achieving an accuracy rate of 74%.