Education Data Science Lab

Ingra. Jessica Liset Martínez de Águila

Machine Learning • Learning Analytics • AI for Education

About the Lab

The Education Data Science Lab explores how artificial intelligence, machine learning and spatial analytics can improve educational research and decision-making. Our work integrates **data science, education research and advanced analytics** to analyze learning environments and educational systems.

Research Areas

Machine Learning

Predictive models for student performance, engagement and dropout risk.

Learning Analytics

Analysis of learning data from online platforms to understand student behavior.

Spatial Data Science

Geospatial analysis of educational systems and regional access to education.

AI in Education

Exploring neural networks and NLP techniques for educational research.

Research Projects

Machine Learning Research Lab

Experiments with regression, classification, clustering and deep learning.

Dropout Prediction Model

Predictive modeling for identifying students at risk of dropping out.

Spatial ML for Education Access

Machine learning and geospatial analysis to study education accessibility.

Educational Data Sources

Selected Publications

AI in Mathematics Education

Research on artificial intelligence and computational thinking in mathematics education.

Learning Analytics for Online Education

Study of engagement patterns in digital learning environments.

Spatial Analysis of Educational Access

Research exploring geographic inequalities in education.

Contact

GitHub github.com/Jekaguila

LinkedIn LinkedIn Profile

Email doulus.jefis@gmail.com