UNIVERSITY STUDENT ACADEMIC PERFORMANCE AND ENGAGEMENT ANALYTICS DASHBOARD

Authors

  • 1 J Priyanka, 2 Sriram Ganesh, 3 K Mahesh Reddy, 4 S Ramcharan Teja, 5 R Arun Author

DOI:

https://doi.org/10.64751/

Abstract

The University Student Academic Performance and Engagement Analytics Dashboard is a data-driven solution designed to analyze and visualize key factors influencing student success using Power BI. The system integrates multiple data sources, including academic records, attendance, and student engagement metrics, to provide a comprehensive overview of student performance and behavior. By consolidating these datasets into a unified platform, the dashboard enables institutions to gain deeper insights into patterns that impact learning outcomes. Leveraging the advanced data modeling and interactive visualization capabilities of Power BI, the system transforms raw and complex data into intuitive, meaningful insights through dynamic reports and dashboards. It allows users to monitor key performance indicators such as grades, attendance rates, and engagement levels, while also identifying trends and correlations that may affect academic achievement. The dashboard includes interactive features such as filters, drill-down capabilities, and real-time data updates, enhancing user experience and analytical flexibility. Furthermore, the system plays a crucial role in early identification of at-risk students by highlighting patterns of low performance or reduced engagement. This enables educators and administrators to take proactive measures, such as providing targeted support or intervention strategies. Overall, the dashboard improves decision-making, enhances student monitoring, and contributes to better academic outcomes by offering a clear, data-driven approach to student performance analysis.

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Published

2026-06-06

How to Cite

1 J Priyanka, 2 Sriram Ganesh, 3 K Mahesh Reddy, 4 S Ramcharan Teja, 5 R Arun. (2026). UNIVERSITY STUDENT ACADEMIC PERFORMANCE AND ENGAGEMENT ANALYTICS DASHBOARD . International Journal of Data Science and IoT Management System, 5(2(2), 813-822. https://doi.org/10.64751/