Predicting science learning outcomes of elementary students in rural area using machine learning algorithms - Harefa, Edward (Innovative: Journal Of Social Science Research, 2024)
Machine learning has paved the way for research focused on improving learners' learning experiences and helping to address challenges faced by the education system. ML technologies analyze data to recognize patterns and use them to make predictions. This research introduces ML models that classify and predict learners' academic success by utilizing ML algorithms such as random forest, support vector machines, gradient boosting, decision tree, logistic regression, Extreme gradient boosting (XGBoost), and deep learning. This research aims to predict students' academic success based on historical data and identify key factors that affect students' academic success. Thus, the proposed approach offers a solution to predict learners' academic performance efficiently and accurately by comparing several ML models with Deep Learning models. The results show that Extreme Gradient Boosting (XGBoost) can predict students' academic performance with 97.12% accuracy. In addition, the results of this study indicate the presence of significant social and demographic features that affect the academic success of these participants. The study concludes that the application of ML algorithms in the classroom will help educators identify gaps in student learning and enable early detection of underperforming students, thus empowering educators in decision-making.