Developing a Dynamic Decision-Support Framework for Higher Education Management Systems through Real-time Information Extraction

Roman Pantin

Abstract: In the changing world of education, it is crucial for institutions to have the ability to make well informed decisions based on data. This paper presents an innovative Decision Support Framework (DDSF), for managing higher education systems. The DDSF utilizes real time information extraction to improve the decision-making process. It combines data analytics and user centric design to provide insights to education administrators. By gathering data from sources like records, financial reports and social media analytics the DDSF uses natural language processing (NLP) and machine learning (ML) algorithms to extract and interpret real time information. This information is then organized in a manner for analysis. The framework is adaptable allowing for the inclusion of emerging data streams ensuring relevance and usefulness. To evaluate the effectiveness of the DDSF we conducted a pilot study at a university. The results indicated improvements in meeting student needs optimizing resource allocation and enhancing operational efficiency. The framework also enables policy development by foreseeing challenges and identifying opportunities, within the education sector. The DDSF brings about a way of managing education by providing a strong platform for institutions to succeed in a competitive and constantly evolving environment. It emphasizes the significance of extracting real time information to guide planning and operational excellence. Future studies will concentrate on expanding the framework, for use, across institutions and incorporating analytics to improve foresight abilities.