COGNIS: Academic Stress Early Warning System

Detecting burnout before it happens. A rule-based early warning system that monitors behavioral academic signals to flag student stress risk early.

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🌟 Overview

Educational institutions typically detect student stress only after academic performance declines. COGNIS proposes a proactive solution that analyzes real-time academic behavior—like attendance drops and submission delays—to calculate an explainable Stress Risk Score (0-100).

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🏗️ Project Architecture

The system consists of three main components working in harmony:

1. Backend (FastAPI): The brain of the system. It houses the Rule Engine, manages the data store, and exposes REST endpoints.
2. Admin Dashboard (React): A professional interface for faculty and administrators to monitor student health, view "At-Risk" lists, and perform "What-if" simulations.
3. Mobile App (Flutter): A personalized student portal where individuals can track their stress levels, view workload graphs, and receive tailored recommendations.

graph TD
    A[Student Mobile App - Flutter] <--> B(FastAPI Backend)
    C[Admin Dashboard - React] <--> B
    B --> D{Rule Engine}
    D --> E[Risk Level: Low/Mod/High]
    B --> F[(Simulated Data Store)]

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🛠️ Key Features

• Predictive Intelligence Lab (What-If Simulator): High-fidelity simulation environment with granular range sliders for:
  • Attendance Rate (%)
  • Weekly Workload (Tasks)
  • Late & Missed Submissions
• Liquid-Smooth Feedback: Zero-latency Risk Meter and Gauge that react instantly to student metric adjustments.
• Explainable Rule Fusion: Real-time natural language explanations for every rule triggered or removed (e.g., "✓ Removed: Attendance below 75%").
• Admin Dashboard: Real-time risk heatmaps, student analytics, and intervention tracking.
• ML + Rule Fusion: Combines pre-trained ML models with a robust, domain-expert rule engine for high precision.
• Stress Trend Visualization: 8-week history tracking using interactive line charts.

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🚀 Getting Started

1. Prerequisites

• Python 3.9+
• Node.js & npm
• Flutter SDK

2. Backend Setup

# Navigate to root
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r app/requirements.txt
python app/main.py

API docs available at: http://localhost:8000/docs

3. Admin Dashboard Setup

cd admin-dashboard
npm install
npm run dev

4. Mobile App (Flutter) Setup

cd stress_monitor
flutter pub get
flutter run

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👥 Contributors

This project was built with ❤️ by:

Name	GitHub	LinkedIn
Rohith Kanna S	Rohithkannas	LinkedIn
Sudhan S	sudhans18	LinkedIn

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📜 License

This project is licensed under the MIT License.