ClassPulse

This project aims to develop an AI-powered Attendance, Behavior and Learning Pattern Analysis System that helps identify students’ academic progress and potential risk factors at an early stage. Traditional attendance systems only record presence, but they do not provide insights into a student’s engagement, performance trends, or behavioral changes. This research-based system combines multiple indicators—attendance consistency, assignment completion, test performance, participation levels, and behavioral patterns—to generate a comprehensive learning profile for each student. Using machine learning techniques such as classification and clustering, the system predicts at-risk students, analyzes correlations between attendance and academic performance, and provides automated recommendations for improvement. The goal is to support teachers with data-driven insights and promote personalized, timely academic intervention to enhance overall learning outcomes.