Thesis Abstract

Abstract
The pursuit of student success in higher education has become a focal point for academic institutions seeking to enhance student outcomes and retention rates. This thesis explores the application of machine learning techniques to analyze the factors influencing student success in higher education. By leveraging advanced computational tools and algorithms, this study aims to uncover patterns and relationships within student data that can be used to predict and enhance student success. Chapter 1 provides an introduction to the research topic, outlining the background of the study, the problem statement, research objectives, limitations, scope, significance of the study, and the structure of the thesis. Chapter 2 presents a comprehensive literature review that synthesizes existing research on student success factors and machine learning applications in education. Chapter 3 details the research methodology, including data collection methods, data preprocessing techniques, feature selection, model development, and evaluation strategies. The chapter also discusses ethical considerations and potential biases in the data analysis process. In Chapter 4, the findings of the study are presented and discussed in detail. Machine learning models are applied to student data to identify key factors influencing student success, such as academic performance, socio-economic background, engagement levels, and support services. The results of the analysis provide valuable insights into the complex interactions between these factors and student outcomes. Chapter 5 serves as the conclusion and summary of the thesis, highlighting the key findings, implications for practice, and recommendations for future research. The study contributes to the growing body of literature on student success in higher education and demonstrates the potential of machine learning techniques to enhance educational outcomes. In summary, this thesis offers a comprehensive analysis of the factors influencing student success in higher education using machine learning techniques. By leveraging the power of data-driven insights, academic institutions can better understand and support their students, ultimately leading to improved retention rates and overall student success.

Thesis Overview

The project titled "Analysis of Factors Influencing Student Success in Higher Education Using Machine Learning Techniques" aims to investigate the various factors that contribute to student success in higher education, with a focus on utilizing machine learning techniques for analysis. This research overview provides a comprehensive explanation of the project, outlining its significance, objectives, methodology, and expected outcomes. ### Significance of the Project Student success in higher education is a critical area of research, as it directly impacts the quality of education, student retention rates, and overall academic performance. By identifying the key factors influencing student success, institutions can implement targeted interventions to support student learning and improve overall outcomes. Machine learning techniques offer a powerful tool for analyzing complex datasets and uncovering patterns that traditional statistical methods may overlook. ### Objectives of the Project 1. To identify the key factors that influence student success in higher education. 2. To explore the potential of machine learning techniques in analyzing student performance data. 3. To develop predictive models that can forecast student success based on various factors. 4. To provide insights and recommendations for improving student support programs and academic policies. ### Methodology The research will involve collecting and analyzing a diverse set of data, including student demographics, academic performance, attendance records, and socio-economic background. Machine learning algorithms such as regression analysis, decision trees, and clustering techniques will be applied to identify patterns and correlations among the variables. The research methodology will also include data preprocessing, model training, validation, and evaluation to ensure the accuracy and reliability of the results. ### Expected Outcomes By the end of the project, we anticipate identifying the key factors that significantly impact student success in higher education. The development of predictive models using machine learning techniques will enable us to forecast student outcomes and provide early interventions to support at-risk students. The research findings will contribute to the existing literature on student success and provide valuable insights for educators, policymakers, and institutions seeking to enhance student learning and academic achievement. In conclusion, the project "Analysis of Factors Influencing Student Success in Higher Education Using Machine Learning Techniques" represents a significant effort to leverage advanced analytical tools for understanding and improving student success in higher education. By exploring the complex interplay of factors influencing student outcomes, this research aims to inform evidence-based practices and interventions that promote student engagement, retention, and academic success.