Predictive Modeling for Student Academic Performance Using Machine Learning Techniques

Thesis Abstract

Abstract
This thesis explores the application of predictive modeling using machine learning techniques to analyze and predict student academic performance. The study aims to leverage the power of data analytics to enhance educational outcomes by identifying key factors that influence student success and developing predictive models to forecast student performance. The research is motivated by the increasing availability of educational data and the growing importance of data-driven decision-making in the field of education. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The chapter sets the stage for the study by highlighting the need for predictive modeling in improving student academic performance. Chapter 2 consists of a comprehensive literature review that examines existing research on student academic performance prediction, machine learning techniques, and their applications in education. The review synthesizes key findings and identifies gaps in the literature that the current study seeks to address. Chapter 3 outlines the research methodology employed in this study. It includes details on data collection, preprocessing, feature selection, model development, evaluation metrics, and validation techniques. The chapter also discusses ethical considerations related to data privacy and confidentiality. Chapter 4 presents a detailed discussion of the findings from the predictive modeling analysis. The chapter highlights the performance of various machine learning algorithms in predicting student academic performance and identifies the most influential factors that contribute to student success. The results are analyzed in the context of existing literature and implications for educational practice are discussed. Chapter 5 concludes the thesis by summarizing the key findings, implications, and contributions of the study. The chapter also discusses the limitations of the research and suggests directions for future research in the field of predictive modeling for student academic performance using machine learning techniques. Overall, this thesis contributes to the growing body of research on data-driven decision-making in education and demonstrates the potential of predictive modeling to enhance student outcomes. By leveraging machine learning techniques and educational data, this study offers valuable insights for educators, policymakers, and researchers seeking to improve student academic performance and support student success.