Student Performance Analysis: A Systematic Research

Abstract

In the rapidly evolving landscape of education, understanding and improving student performance is of paramount importance. This research paper explores the application of Machine Learning (ML) technologies within the realm of Big Data Analytics to comprehensively analyze and enhance student performance. Leveraging the vast amount of data generated within educational institutions, this study demonstrates how ML algorithms and techniques can be harnessed to gain insights into student learning patterns, predict academic outcomes, and develop data-driven strategies for educational improvement. The paper begins by highlighting the significance of student performance analysis in modern education and the challenges faced by institutions in managing and interpreting the growing volume of educational data. It then presents a comprehensive review of the state-of-the- art ML algorithms and data processing techniques relevant to student performance analysis. Furthermore, the research outlines a novel framework for student performance analysis, integrating various ML models such as regression, classification, and clustering, alongside advanced data preprocessing techniques. The proposed framework is designed to handle diverse educational datasets, including academic records, attendance records, socio-demographic information, and learning resources utilization[1] . Through a series of experiments and case studies, this paper demonstrates the practical application of ML in predicting student performance accurately, identifying at-risk students, and personalizing educational interventions. It also delves into the ethical considerations and data privacy concerns associated with the use of student data in ML-based educational analytics. The results and insights from this research offer valuable implications for educational institutions, policymakers, and researchers alike. By harnessing the power of Big Data and ML technologies, institutions can make data-driven decisions to enhance teaching methodologies, improve student support systems, and ultimately elevate student success rates. Additionally, this study contributes to the ongoing discourse on the responsible use of data in education, emphasizing the importance of transparency, fairness, and ethical considerations. In conclusion, this research paper presents a robust framework that showcases the potential of Machine Learning technologies within the realm of Big Data Analytics for student performance analysis. It offers a roadmap for educational institutions to harness the power of data to foster better learning outcomes and contributes to the ongoing dialogue on responsible data usage in education.