Predictive analytics is a crucial tool in changing teaching and learning practices in the ever-changing field of educational technology. This study examines the dynamic function of predictive analytics in customizing education, with a specific emphasis on its ability to adapt learning paths to improve individual student achievement. The study examines how predictive models might identify distinct learning patterns and demands by assessing many data sources, such as academic achievement, learning habits, and engagement indicators. It showcases the capabilities of these analytics in generating adaptive learning experiences, thereby providing a more focused approach to teaching. This article investigates how predictive analytics facilitates the early detection of educational hazards, allowing for timely interventions to support students who are at danger of academic underperformance or dropping out.
Higher education institutions face a problem with student turnover that has many aspects and affects both students and universities in different ways. Using predictive analytics and machine learning, this study shows a new way to deal with this problem. The main goal is to create predicting algorithms that can predict which students are most likely to drop out, so colleges can get involved in their lives in a timely and effective way. As part of this method, the authors collect and preprocess a large dataset from different university records. This dataset includes information about academic success, socioeconomic background, participation in campus activities, and psychological health. The study uses advanced machine learning methods to look at all of these different data points. It focuses on feature selection and engineering to find the most important factors that predict student dropout. Rigid validation methods are used to test how well the model works, making sure that it can accurately and reliably predict the future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.