Interactive Dashboard Development for Student Performance Monitoring: Integrating Academic and Socio-Demographic Data

Abstract

Strategic decision  making in institutional settings is often constrained by the fragmentation and heterogeneity of data across multiple sources. This study addresses this critical gap by developing and validating an interactive web-based dashboard designed to consolidate and transform heterogeneous institutional data from seven distinct sources into actionable insights. A complex feature engineering pipeline was necessitated, involving comprehensive data integration and structural consistency checks. Techniques like Text Normalization and Feature Mapping were applied to clean over a lot of inconsistent entries, alongside Feature Binning and Extraction to generate analytically robust metrics. The system was implemented using Python for data processing and ReactJS for the dynamic interface, and its viability was validated via structured User Acceptance Testing (UAT). The subsequent descriptive analysis provided key insights into student demographics, geographical reach, and enrollment compliance across academic levels. Crucially, the comprehensive UAT resulted in an outstanding overall acceptance score of very worthy. However, feedback analysis indicated a dominant user focus on visual aspects, with noted complaints regarding the suboptimal color scheme and contrast impacting user experience. The findings confirm that complex feature engineering is a viable and effective strategy for transforming fragmented institutional data into an immediately deployable strategic resource. This system offers a validated blueprint for data consolidation in higher education. Future work is accordingly  directed toward revising the color palette and contrast ratios to enhance visual clarity and user experience, alongside continuous optimization of data completeness to maintain the dashboard’s utility