Campus Connect: A Smart College Management and Student Performance Prediction System
Karan Sathe¹, Rituja Sonawane², Samruddhi Harishchandre³,Dnyaneshwari Telbhare⁴,
Prof. M. S. Bhosale5
¹Department of Information Technology, Sinhgad College of Engineering, Pune-41
²Department of Information Technology, Sinhgad College of Engineering, Pune-41
³Department of Information Technology, Sinhgad College of Engineering, Pune-41
⁴Department of Information Technology, Sinhgad College of Engineering, Pune-41
5Department of Information Technology, Sinhgad College of Engineering, Pune-41
Email : karansathe.scoe.it@gmail.com
ABSTRACT - Contemporary academic environments struggle with operational inefficiencies stemming from disparate information repositories, lack of actionable intelligence on student trajectories, and siloed communication channels across institutional stakeholders. This paper introduces Campus Connect, a comprehensive software ecosystem that consolidates administrative operations and applies predictive analytics to forecast student academic outcomes. Leveraging the MERN technology stack combined with a Flask-based machine learning microservice, the platform delivers differential user interfaces for students, faculty, and administrators with role-specific visualizations and controls. Performance prediction employs an optimized Random Forest ensemble method analyzing attendance patterns, continuous assessment scores, cumulative performance indices, and course credit completion. Technical validation demonstrates model effectiveness at 89.33% accuracy, sub-2-second latency across user interactions, and horizontal scalability supporting simultaneous sessions exceeding 2000 concurrent participants. This contribution advances educational data mining through an integrated, production-ready implementation bridging institutional data governance, contemporary full-stack architecture, and automated predictive intelligence for timely intervention with struggling learners.
· Keywords: Educational Management Platform, Predictive Analytics, Student Academic Forecasting, MERN Technology Stack, Ensemble Learning Methods, Data-Driven Academic Support, Multi-Role Web Application
