Prediction of School Dropout Risk and Intervention Optimization
A metropolitan US School district faced the challenge of reducing the dropout rate across all its high schools.
We implemented a solution that alerts counselors about students with a higher risk of dropping out, enabling them to intervene in a timely manner to reverse the situation.
Additionally, we optimized the allocation of special courses by placing students who would benefit the most in these programs.
The solution consists of an intranet Web Portal that provides real-time information on the expected dropout rate for the current year and the individual risk of each student. It also allows for evaluating the most suitable special course for each student and accessing historical data.
With this solution, counselors and school administrators can make timely and informed decisions supported by implemented machine learning models.
- As a result of this implementation, there was an overall decrease in the county’s dropout rate by Y%.
- Better planning of offered courses allowed for a Z% reduction in the previous year’s budget.
A notable improvement was observed in the relationship between students and counselors, which continued to grow month by month.