A Comparative Machine Learning Approach for Forecasting Student Achievement Based on Academic and Behavioral Features - Madaan, Nisha; Sharma, Sanjay Kumar (International Journal of Computer Sciences and Engineering, 2025)
Forecasting student achievement is essential in educational environments to enhance academic success and reduce dropout rates. This research focused on improving student performance prediction through integrating advanced machine learning (ML) techniques. The dataset from Kaggle contains 20 features of the student`s academic, demographic, motivational, and other. The study showed a strong relationship between the students` academic achievement and their academic data, like previous exam scores, regularity, sleeping hours, study hours, and demographic features, as well as some other features. Students` academic achievement is predicted using the ML model. Then, these features were input into various ML models, such as LR, RF, KNN, SVM, DT, and NB. The experimental results show that the SVM model outperformed other models after applying hyperparameter tuning on the models and achieved the maximum classification accuracy of 0.9440. Also, a comparison of ML models evaluated through metrics has been presented. Also, graphical analysis shows that attendance, previous exam score, and study hours have a higher impact on a student’s academic achievement. This paper promotes the enhancement of quality education and the development of student skills as essential components for workforce advancement and sustainable industrialization, following the UN Sustainable Development Goals (SDGs) aims. By using this technique, teachers will enable students to monitor their academic progress according to their performance and adjust their study habits accordingly.