Abstract
Decision support system (DSS) for educational management based on big data leverage advanced analytics and machine learning techniques to enhance decision-making processes within educational institutions. By collecting and analyzing large volumes of data from various sources such as student performance, administrative records, and institutional resources, the DSS provides valuable insights and predictive models to administrators and educators. These insights can inform strategic planning, resource allocation, student interventions, and curriculum development, ultimately improving student outcomes and organizational efficiency. With the ability to process vast amounts of data in real-time, the DSS enables timely and data-driven decision-making, empowering educational leaders to address challenges and capitalize on opportunities in today's complex educational landscape.The integration of big data analytics with the Recommender Ranking Decision Support System (RRDS) presents a transformative approach to enhancing educational management. This paper explores the application of big data analytics within the educational setting, focusing on the development and implementation of the RRDS. By leveraging large and diverse datasets, the RRDS enables personalized learning experiences, optimizes resource allocation, and enhances student outcomes. Through predictive modeling, the RRDS predicts student performance and identifies at-risk students, facilitating targeted interventions to support student success and retention. Additionally, the system offers personalized learning recommendations tailored to individual student needs, fostering engagement and motivation. The findings underscore the significance of data-driven decision-making in improving educational practices and institutional performance. As educational institutions increasingly embrace technology-driven solutions, the integration of big data analytics and RRDS stands poised to revolutionize educational management, ultimately leading to more effective teaching and learning methodologies and better outcomes for students.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.