Imagine having a GPS for a student's entire school journey instead of just a snapshot of their grades today. This work tracks how kids learn over many years to figure out exactly when they start falling behind. By spotting these patterns, schools can step in with the right help at the right time to keep students from dropping out.
Schools and policymakers lack a clear, comparable way to track student progress over time, making it difficult to identify exactly when and why students drop out or fail to acquire basic skills.
A conceptual model mapping 70 variables across four clusters (student, family, teacher, school) and a feasibility study on the comparability of longitudinal databases.
If you are a software provider dealing with high student churn and low engagement — this project developed a conceptual model with 70 variables that identifies how family and school factors shape learning. You can use these insights to build more personalized learning paths that prevent students from falling behind.
If you are a city official dealing with high early school leaving rates — this project developed a mapping of longitudinal datasets across 32 European countries. This allows you to implement targeted interventions based on evidence of what actually works to keep 6-18 year-old students engaged.
If you are a consultant dealing with a lack of comparable data for school audits — this project developed a feasibility study on making different educational databases directly comparable. This enables you to offer high-value comparative analysis services to schools across Southern and Western Europe.
Based on available project data, no pricing or cost information is provided as this is an EU-funded research project.
The project analyzes data from 32 European countries, suggesting the findings are scalable across different national education systems.
Based on available project data, specific licensing terms are not mentioned, though it involves cooperation with national authorities to access administrative sources.
The project focuses on the comparability of databases and the use of longitudinal data, which can inform how data is collected and integrated into school systems.
The project period runs from 2024-02-01 to 2027-01-31.
The consortium is heavily academic, consisting of 15 partners including 8 universities and 5 research centers. There is very low industrial presence, with only 1 industry partner representing a 7% ratio, indicating the project is currently driven by scientific discovery rather than immediate commercial product development.
Contact Fondazione per la Scuola in Italy for details on data mapping results.
Contact us to find out how to apply these 70 educational variables to your EdTech product.
