Key Insights: Built a data analytics solution to identify risk factors and predict academic outcomes for high school students, helping address dropout rates that can reach over 40% in developing countries.

About the Client

Our clients were a national government agency overseeing secondary education and the National Institute of Educational Evaluation in Uruguay. They manage educational policy and assessment across the country’s high schools.

The Challenge

Student dropout is a major challenge for education systems, with rates ranging from 5% to over 40% in developing countries. Early identification of students at risk is critical to designing effective prevention strategies.

The goal of the project was to analyze socio-economic and academic data for over 100,000 students to detect risk factors and understand the patterns that lead to dropout.

Marvik’s Approach

We developed a data analytics solution that:

• Identified vulnerability factors linked to higher dropout risk.
  
  
• Performed clustering to group students by educational trajectories.
  
  
• Generated predictions of a student’s academic outcome for a given year.
  

The project used Python along with advanced data analysis, clustering, and visualization techniques.

The Results & Impact

• Provided actionable insights to guide intervention strategies.
  
  
• Allowed early identification of at-risk students across a national dataset.
  
  
• Gave policymakers and educators the tools to better allocate resources and improve retention rates.
  

With dropouts impacting long-term social and economic outcomes, this approach empowers education systems to intervene before it’s too late.

Why This Matters

By applying analytics at scale, this project helps shift education systems from reactive to proactive, improving the chances of success for thousands of students.