Developing a Machine Learning-based System for Predicting Student Performance in Online Learning Environments

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the development of a Machine Learning-based System for Predicting Student Performance in Online Learning Environments. The rapid growth of online education has led to an increasing demand for effective tools that can predict and enhance student outcomes. Machine Learning techniques offer a promising approach to address this need by leveraging data analytics to predict student performance and provide personalized interventions. The primary objective of this research is to design and implement a predictive system that can accurately forecast student performance in online learning environments. The study begins with a thorough literature review in Chapter Two, which explores existing research on student performance prediction, Machine Learning algorithms, and their applications in educational contexts. Chapter Three outlines the research methodology, including data collection, preprocessing, feature selection, model development, and evaluation metrics. The proposed system integrates various Machine Learning algorithms, such as Decision Trees, Random Forest, and Support Vector Machines, to create predictive models based on student data. Chapter Four presents a detailed discussion of the findings, including the performance evaluation of the developed models and the comparison of different algorithms. The results demonstrate the effectiveness of the predictive system in accurately forecasting student performance metrics, such as grades and course completion rates. The discussion also highlights the strengths and limitations of the system, as well as potential areas for future research and improvement. In conclusion, this thesis provides a significant contribution to the field of educational technology by showcasing the potential of Machine Learning in predicting student performance in online learning environments. The developed system offers valuable insights for educators, administrators, and policymakers to enhance student outcomes and support personalized learning experiences. By leveraging advanced data analytics techniques, this research opens up new possibilities for improving the effectiveness and efficiency of online education platforms.