An intelligent learning analytics platform that helps educators identify student behavioral patterns and provide personalized learning recommendations using machine learning.
The AI Student Insight System is designed for K-12 educators and administrators to:
- Analyze student behavior patterns using machine learning
- Identify at-risk students early for timely intervention
- Generate personalized learning recommendations based on individual profiles
- Track class-wide trends through batch analysis
The system classifies students into three behavioral categories:
- 🔴 At Risk - Students needing immediate attention and support
- 🟡 Balanced - Students performing within expected parameters
- 🟢 High Performer - Students excelling academically
Upload a CSV file containing student data to analyze your entire class at once. Get instant insights including:
- Behavior distribution charts
- At-risk student identification
- Downloadable reports with recommendations
Enter individual student information to generate a personalized assessment including:
- Behavioral prediction
- AI usage impact analysis
- Recommended content strategy
- Study plan suggestions
- Monitoring frequency recommendations
Switch between dark and light themes for comfortable viewing in any environment.
- Python 3.9 or higher
- pip (Python package manager)
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Clone the repository
git clone https://github.com/Shiva-129/Personalized-Learning-System-for-K-12-Students.git cd Personalized-Learning-System-for-K-12-Students -
Install dependencies
pip install -r requirements.txt
-
Run the application
streamlit run app.py
-
Open in browser Navigate to
http://localhost:8501
The system uses a Random Forest Classifier trained on student behavioral data to predict outcomes based on:
| Feature Category | Examples |
|---|---|
| Demographics | Age, Grade Level, Gender |
| Study Habits | Study hours, Sleep hours, Social media usage |
| Academic Performance | Attendance, Exam scores, Assignment averages |
| AI Usage | AI tools used, Usage purpose, Dependency level |
The model combines these features with engineered metrics like:
- Study-life balance ratio
- Engagement score
- Risk indicators
- Learning efficiency
- Frontend: Streamlit
- Machine Learning: scikit-learn (Random Forest)
- Data Processing: Pandas
- Styling: Custom CSS with dark/light theme support
├── app.py # Main Streamlit application
├── behavior_model.pkl # Trained ML model
├── label_encoder.pkl # Label encoder for predictions
├── feature_list.pkl # Feature configuration
├── requirements.txt # Python dependencies
├── .streamlit/
│ └── config.toml # Streamlit configuration
└── README.md # This file
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License.
Shiva - GitHub Profile
Made with ❤️ for better education