Smart Study Tracker with Class Room Collaboration
Mentore :- Mr. Manish Vasant Salvi
Author :-
Mohammed Ansari
Shakib Khan
Samir Khan
Yvuraj Shing
ABSTRACT
In modern academic environments, students rely on multiple fragmented tools for managing coursework, tracking study progress, and collaborating within classrooms. Platforms such as classroom management systems, personal note-taking tools, and productivity trackers operate independently, resulting in inefficient workflows, lack of centralized data, and minimal insight into individual learning patterns. This fragmentation reduces students' ability to effectively monitor their academic performance and limits the potential for personalized learning.
To address these limitations, this paper proposes a Smart Study Tracker with Classroom Collaboration System, an integrated academic platform designed to unify study tracking, assignment management, classroom interaction, and intelligent analytics within a single ecosystem. The system enables students to monitor their study habits, manage assignments, participate in classroom discussions, and receive personalized recommendations based on their academic behavior.
The proposed system is built using a MERN (MongoDB, Express.js, React.js, Node.js) stack for the frontend and backend infrastructure, combined with a FastAPI-based microservice to handle artificial intelligence functionalities. The platform incorporates multiple intelligent modules, including an AI-powered academic chatbot for instant query resolution, a recommendation engine for personalized study suggestions, and a predictive analytics module that evaluates student performance trends using historical data.
Additionally, the system includes a structured classroom collaboration layer that allows educators to create courses, assign tasks, monitor student progress, and analyze engagement metrics. The analytics dashboard provides visual insights into performance indicators such as study consistency, assignment completion rates, and subject-wise proficiency levels. Machine learning models are integrated to identify learning gaps and suggest targeted improvements.
The implementation emphasizes scalability, real-time interaction, and modular architecture, ensuring that the platform can adapt to diverse academic environments. Experimental observations and simulated usage scenarios indicate that the proposed system improves student engagement, enhances productivity, and supports data-driven academic decision-making. The integration of artificial intelligence further enables adaptive learning experiences tailored to individual student needs.
KEYWORDS
Study Tracker, Artificial Intelligence, Learning Analytics, Classroom Collaboration, Academic Performance Prediction, MERN Stack, FastAPI, Educational Technology, Personalized Learning, Student Productivity System
