Student performance evaluation in traditional academic environments primarily relies on final examination results and periodic assessments. However, such approaches often overlook behavioral and continuous performance indicators such as attendance consistency, assignment submission patterns, internal assessment progression, and classroom participation metrics. The integration of data analytics within academic institutions enables systematic monitoring, predictive evaluation, and data-driven academic decision-making. This study presents a comprehensive analytical framework for the development of a Student Performance and Behavioral Analytics System using Microsoft Power BI as a business intelligence platform. The proposed system integrates structured academic datasets including attendance records, internal examination scores, assignment completion rates, and final semester results into an interactive dashboard architecture. By leveraging data transformation tools, calculated measures, relational modeling, and visualization components, the system enables multidimensional analysis of student performance trends. Statistical correlation modeling is applied to evaluate relationships between attendance percentage and final academic outcomes, while categorical classification algorithms segment students into high-performing, average-performing, and at-risk groups. The research further analyzes system scalability, usability efficiency, and real-time dashboard responsiveness within institutional environments. Findings indicate that visual analytics significantly enhances faculty monitoring capabilities, reduces manual evaluation time, and supports early academic intervention strategies. Although the system improves transparency and monitoring efficiency, challenges related to data accuracy, integration with legacy academic systems, and user training remain relevant considerations. Overall, the proposed analytics framework demonstrates the transformative potential of business intelligence tools in modern educational ecosystems.
Student Performance Analytics, Academic Monitoring, Power BI Dashboard, Data Visualization, Educational Data Mining, Performance Classification, Attendance Analysis, Decision Support System, Behavioral Indicators, Academic Intelligence.
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