Thesis Abstract

Abstract
This thesis presents a comprehensive investigation into the application of machine learning algorithms for predicting student performance in higher education. The study aims to address the growing need for effective tools and strategies to enhance student outcomes and academic success. By leveraging the power of machine learning techniques, this research seeks to develop predictive models that can accurately forecast student performance based on various input factors and parameters. The study begins with an introduction that highlights the significance of the research topic and outlines the objectives and scope of the study. A detailed literature review is conducted to examine existing research and theories related to student performance prediction, machine learning algorithms, and their applications in education. The review encompasses ten key areas that provide a solid foundation for the research methodology and analysis. The research methodology chapter outlines the approach and techniques used to collect and analyze data for developing the predictive models. Various data sources, sampling methods, and data preprocessing techniques are discussed to ensure the reliability and validity of the research findings. The chapter also includes a detailed description of the machine learning algorithms selected for the study, including their strengths, weaknesses, and suitability for predicting student performance. In the findings and discussion chapter, the results of the predictive models are presented and analyzed in detail. The performance of the machine learning algorithms is evaluated based on key metrics such as accuracy, precision, recall, and F1 score. The chapter also explores the factors that influence student performance and the implications of the predictive models for educational institutions and stakeholders. The conclusion and summary chapter provide a comprehensive overview of the research findings, implications, and recommendations for future research and practice. The study highlights the potential of machine learning algorithms to revolutionize student performance prediction and enhance educational outcomes in higher education settings. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning algorithms in education and provides valuable insights into the development of predictive models for improving student performance. The findings have significant implications for educators, administrators, and policymakers seeking to enhance learning outcomes and support student success in higher education institutions.

Thesis Overview

The project titled "Applying Machine Learning Algorithms for Predicting Student Performance in Higher Education" aims to explore the potential of machine learning techniques in predicting student performance in higher education settings. This research seeks to address the growing need for personalized and data-driven approaches to enhance student success and academic outcomes. By leveraging machine learning algorithms, this study intends to analyze a wide range of data points related to student demographics, academic history, learning behaviors, and performance indicators. Through the application of predictive modeling techniques, the research aims to develop accurate models that can forecast student performance with a high degree of precision. The significance of this research lies in its potential to revolutionize the way educators and institutions approach student support and intervention strategies. By identifying early indicators of at-risk students and predicting their performance trajectories, educators can proactively intervene and provide targeted support to enhance student retention and success rates. The methodology for this research will involve collecting and preprocessing large volumes of student data, including demographic information, course enrollment records, grades, and assessment results. Various machine learning algorithms such as decision trees, logistic regression, and neural networks will be employed to build predictive models based on the processed data. The findings of this research are expected to shed light on the effectiveness of machine learning algorithms in predicting student performance and inform the development of data-driven strategies to support student success in higher education. Through a thorough analysis of the results, this study aims to provide actionable insights for educators, administrators, and policymakers to enhance student outcomes and improve overall academic performance. Overall, this research project represents a significant step towards leveraging the power of machine learning in the field of education to create more personalized and effective support systems for students in higher education institutions. By harnessing the predictive capabilities of machine learning algorithms, this study aims to contribute to the advancement of student success initiatives and the overall improvement of academic outcomes in higher education settings.