SmartAssign: An Intelligent Assignment Submission and Feedback System

Abstract

In order to enhance academic evaluation procedures, this study introduces a web-based, machine learning-driven assignment submission and feedback system. The approach tackles two main issues: the physical labor required to complete and assess assignments and the absence of comprehensive, insightful feedback given to students that goes beyond simple grades. The MERN stack (MongoDB, Express.js, React.js, Node.js) is coupled with Python to enable machine learning features in Smart Assign. Teachers and students are its two main user roles. Instructors can develop time-bound multiple-choice questions (MCQs) on a variety of subjects, upload student data via Excel files, and use an interactive dashboard to measure class performance. Only if their IDs are pre-listed may students register, access current exams, turn in answers on time, and get automatic feedback and test results.. The system employs a K-means clustering algorithm to generate topic-wise feedback and categorize student performance based on historical attempt data. For new users without prior data, a rule-based fallback mechanism ensures feedback generation from current attempts. Experimental testing demonstrates accurate automated scoring, reliable role-based access control, and effective ML-based feedback generation, highlighting Smart Assign’s potential to enhance efficiency, scalability, and learning outcomes in modern education systems.