EcoLearn — Gamified Environmental Education Platform for Schools and Colleges

Key Features of EcoLearn

Gamification Engine

At the heart of EcoLearn is a fully functional gamification system designed to keep students motivated and engaged. Users earn Eco Points for every activity they complete — lessons, quizzes, daily tasks, and eco challenges. The platform features six progression levels starting from Seedling all the way up to Earth Champion, with automatic level-up logic built into the backend.

• Six progression levels: Seedling, Sprout, Sapling, Tree, Forest Guardian, Earth Champion
• Nine badge categories: Water Guardian, Air Warrior, Forest Protector, Energy Saver, Waste Warrior, Climate Champion, Eco Learner, Challenge Master, Special Achievement
• Daily task system with streak tracking to build learning habits
• Eco Challenges with evidence submission and teacher review workflow
• Individual and school-level leaderboard for competitive learning

Environmental Education Module

The education module is structured around topics covering climate change, air quality, water conservation, deforestation, and sustainable development. Lessons are categorized by difficulty — Beginner, Intermediate, and Advanced — making the platform suitable for both school students and college learners. Each lesson tracks individual progress, and contextual Eco Facts are displayed alongside content to reinforce key environmental concepts.

• Structured lessons with topic-wise organization and difficulty levels
• Multiple-choice and true/false quizzes with timed attempts and score tracking
• Lesson progress tracking with completion percentages per user
• Contextual Eco Facts displayed per topic for deeper learning

Analytics Dashboard with Machine Learning

EcoLearn includes six live analytics pages powered by nine real-world environmental datasets. Each page renders interactive Plotly charts server-side, giving users visual insights into real data. The ML component uses trained scikit-learn, XGBoost, and LightGBM models to deliver live predictions directly in the browser — a feature that makes this project extremely impressive for final year project vivas and demonstrations.

• Air Quality Index trends across Indian cities using real AQI, PM2.5, PM10, NO2, and SO2 data
• Water potability prediction using a trained Random Forest classifier
• Deforestation pattern analysis with K-Means clustering and choropleth maps
• Global CO2 emissions with India BAU vs Green Transition forecast
• Natural disaster frequency analysis by type, country, and year
• SDG (Sustainable Development Goals) progress tracking with radar charts

The project also includes six Jupyter notebooks covering water quality modeling, air quality analysis, waste classification with MobileNetV2 CNN, deforestation analysis, emissions forecasting, and student performance prediction. Trained model files are saved as .pkl and .h5 files, and the analytics views include intelligent fallback logic if models are not present.

Role-Based User Management

EcoLearn supports three distinct user roles — Student, Teacher, and Eco Club Coordinator — each with tailored dashboards and permissions. The school affiliation system allows users to register under specific schools, colleges, or universities, enabling institution-level leaderboard rankings. Each user gets a personal eco-stats dashboard showing total points, current level, streak days, trees planted, and full point transaction history.