Analysis of Factors Influencing Student Academic Performance Using Machine Learning Algorithms

Thesis Abstract

Abstract
This thesis presents a comprehensive analysis of factors influencing student academic performance through the application of machine learning algorithms. The study aims to identify and analyze the various factors that significantly impact student academic performance in order to develop predictive models that can help educational institutions improve student outcomes. The research methodology involves the collection and analysis of academic data from a diverse sample of students, encompassing demographic information, socio-economic background, study habits, and other relevant variables. Machine learning algorithms such as regression analysis, decision trees, and neural networks are utilized to build predictive models that can accurately forecast student academic performance based on the identified factors. Chapter 1 provides the introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 consists of a detailed literature review covering ten key studies related to student academic performance and machine learning applications in education. Chapter 3 focuses on the research methodology, detailing the data collection process, variables analyzed, machine learning techniques employed, model evaluation methods, and ethical considerations. In Chapter 4, the findings of the study are discussed in depth, highlighting the most influential factors affecting student academic performance as identified by the machine learning models. The analysis includes insights into the relationships between different variables and their impact on academic outcomes. Furthermore, the chapter examines the predictive accuracy and reliability of the developed models in forecasting student performance. Lastly, Chapter 5 presents the conclusion and summary of the thesis, summarizing the key findings, implications, and recommendations for educational institutions and policymakers. The research findings underscore the importance of leveraging machine learning algorithms to gain valuable insights into the factors influencing student academic performance and to support data-driven decision-making in education. Overall, this thesis contributes to the existing body of knowledge on student performance analysis and provides a foundation for future research in the field of educational data analytics.