Project Abstract

Abstract
In recent years, the emergence of online learning environments has revolutionized the traditional educational landscape, providing students with unprecedented access to educational resources and opportunities. However, one of the key challenges faced by educators in these environments is predicting and improving student performance. Machine learning algorithms have shown promise in addressing this challenge by analyzing vast amounts of data to identify patterns and trends that can be used to predict student outcomes. This research project aims to develop a machine learning-based system for predicting student performance in online learning environments. The project will begin with a comprehensive review of existing literature on machine learning, online learning environments, and student performance prediction. This review will provide a solid theoretical foundation for the development of the proposed system. The research methodology will involve collecting and analyzing data from a sample of students enrolled in online courses. Various machine learning algorithms will be applied to the data to develop predictive models for student performance. Chapter four will present a detailed discussion of the findings from the research, including the performance of different machine learning algorithms in predicting student outcomes. The implications of these findings for educators and policymakers will be discussed, along with recommendations for future research in this area. Overall, this research project has the potential to significantly impact the field of education by providing educators with a powerful tool for predicting and improving student performance in online learning environments. By leveraging the capabilities of machine learning, this system has the potential to enhance the educational experience for students and help educators tailor their teaching strategies to better meet the needs of individual learners.

Project Overview