An AI-powered attendance and engagement prediction system for virtual classrooms.
KinMind is an AI-powered system for automating attendance tracking and predicting student engagement in virtual classrooms.
Inspired by Mayan concepts of time (Kin = "day" or "sun" in Yucatec Maya), KinMind merges ancient wisdom with modern data science to help educators understand and improve participation in online learning environments.
This project was developed as part of the graduate course Gestión de la Educación Virtual (Universidad Nacional Autónoma de Honduras, UNAH) and includes:
- Automated attendance extraction from Zoom logs.
- Real-time engagement prediction using machine learning.
- Data anonymization for privacy protection.
- Open-source code and sample anonymized datasets.
- Extracts and processes Zoom connection logs.
- Calculates total and average attendance times.
- Predicts engagement using supervised ML models.
- Generates visualizations for class participation trends.
- Provides anonymized datasets for reproducibility.
# Clone the repository
git clone https://github.com/Mayan-Mind/KinMind.git
cd KinMind
# Create and activate virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txtIf you use KinMind in your research or teaching, please cite:
Palacios, S. (2025). KinMind: Real-Time Prediction of Attendance and Engagement in Virtual Classrooms. GitHub Repository: https://github.com/Mayan-Mind/KinMind
- Code: Licensed under the MIT License © 2025 Mayan Mind, LLC.
- Data: Licensed under the Creative Commons Attribution-NonCommercial 4.0.