Performance Prediction Data Mining System for Disabled Students Using Machine Learning

Abstract

This study aims to explore the educational achievements of individuals aged 21 to 38, specifically examining the differences between those with disabilities and those without. The research delves into the realm of Online Learning Platforms, which are recognized for offering extensive online courses that cater to both educational institutions and individual learners. Additionally, the study investigates Collaboration and Communication Platforms, which are designed to enhance interaction and cooperation among students and educators through various tools like discussion forums, chats, and shared workspaces. Adaptive Learning Platforms: Employing advanced algorithms and data analytics, this study used a dataset covering the UK from July 2013 to June 2020 to examine the highest skill levels of these two different groups. The data set, originally in Excel format, was carefully organized and structured for analytical purposes. The approach included the use of Python libraries such as NumPy for numerical calculations, and Matplotlib for visualization and proposed integration in a cloud-based system. The study's methodology is underpinned by sophisticated data analysis techniques, utilizing Python libraries such as NumPy, renowned for its efficiency in handling complex numerical calculations, and Matplotlib, which offers powerful visualization tools that are instrumental in elucidating the trends and patterns within the data. It is not only robust but also versatile, accommodating the integration of additional Python libraries such as Pandas for data manipulation and SciPy for more advanced scientific computations, thereby enhancing the depth and breadth of the analysis. Furthermore, the proposed integration of this analytical setup into a cloud-based system underscores the study's forward-thinking approach, aiming to leverage the scalability, accessibility, and collaborative potential of cloud computing. This integration promises to streamline the data analysis process, facilitating real-time data processing and enabling a dynamic exploration of the dataset. The study's methodology is underpinned by sophisticated data analysis techniques, utilizing Python libraries such as NumPy, renowned for its efficiency in handling complex numerical calculations, and Matplotlib, which offers powerful visualization tools that are instrumental in elucidating the trends and patterns within the data. This analytical framework is not only robust but also versatile, accommodating the integration of additional Python libraries such as Pandas for data manipulation and SciPy for more advanced scientific computations, thereby enhancing the depth and breadth of the analysis.