Developing a Machine Learning Model for Predicting Student Performance in Online Learning Environments

Thesis Abstract

Abstract
The rapid growth of online learning platforms has revolutionized education by providing flexibility and accessibility to a diverse range of learners. However, the challenge of predicting student performance in online learning environments remains a critical issue that impacts both learners and educators. This thesis presents a comprehensive study on developing a machine learning model to predict student performance in online learning environments. By leveraging the power of machine learning algorithms and educational data mining techniques, this research aims to enhance the understanding of factors influencing student performance and provide insights for personalized learning interventions. The introduction sets the stage by outlining the background of the study, identifying the problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review in Chapter Two critically examines existing research on student performance prediction, machine learning applications in education, and relevant theoretical frameworks. This chapter synthesizes key findings and identifies gaps in the literature that motivate the current study. Chapter Three details the research methodology employed in this study, including data collection procedures, feature selection techniques, model development, and evaluation metrics. The methodology section outlines the steps taken to preprocess and analyze the educational data to build and validate the machine learning model effectively. In Chapter Four, the discussion of findings presents the results of the developed machine learning model for predicting student performance. This chapter delves into the performance metrics, feature importance analysis, model interpretability, and insights gained from the predictive model. The findings elucidate the factors that significantly impact student performance in online learning environments and provide valuable recommendations for educational practitioners. Finally, Chapter Five concludes the thesis by summarizing the key findings, implications for practice, limitations of the study, and directions for future research. The conclusion reflects on the significance of the developed machine learning model in enhancing student performance prediction accuracy and supporting personalized learning strategies in online education. Overall, this thesis contributes to the field of educational technology by offering a novel approach to predicting student performance in online learning environments through the application of machine learning techniques. The insights gained from this research have the potential to inform educational practices, improve student outcomes, and foster a data-driven approach to personalized learning in the digital age.