Assessing Student Outcomes Through Longitudinal and Predictive Analysis
Achungo Children’s Center provides education, care, food, clothing and medical assistance for about 900 orphans and destitute children in rural southwest Kenya.
Project at a Glance:
Team:
Data Scientists
Hours Engaged:
500
Skills & Stack:
SQL, Python, pgAdmin, Supabase, Jupyter Notebook
Problem
Over its years of operation, Achungo Children’s Center has provided education to hundreds of students in Kenya from primary to secondary schools - but tracking these students' outcomes has proven difficult due to the different methods exam scores are collected, measured, and reported back to the organization. This prevented the organization from deriving key insights from the student data that would assess the impact of their programs and enable data-led improvements.
Solution
Matched through Prometheus, data science fellows from The Knowledge House created a longitudinal analysis to understand student outcomes over time based on location and key demographics. Using insights from this analysis, the team conducted a predictive analysis to identify if any factors drove academic success for the students. To create these analyses, the team modeled the historical data into a relational database, allowing Achungo to revisit the analysis in the future.
“Working on a Prometheus project has provided me the confidence to state that I have applied my teachings from a data science boot camp to a real-world scenario. This experience is significant for me as I come from a non-traditional background in tech and am always looking for opportunities to improve and showcase my ability to perform this kind of work despite the learning curve.” - Data Science Fellow (The Knowledge House)