Fritz is an applied econometrician at the University of Leuven where he applies advanced analytics to evaluate policies, mainly in education. He has worked on reports for the European Union, the Ministry of Education and Syntra. Halfway his PhD in Economics he shifted his interests towards machine learning methods. His presentation is the result from joint work with the Bank of Italy and illustrates how machine learning or AI methods can be used to improve school rankings using an Italian dataset.
Machine learning methods can be used to improve the assessment of school quality. School rankings based on value-added (VA) estimates are subject to prediction errors, since VA is defined as the difference between predicted and actual scores of students. More accurate predictions result in more informative school rankings, and better policies. We introduce a more flexible random forest (RF), rooted in the machine learning literature, to minimize prediction errors and to improve school rankings. Monte Carlo simulations demonstrate the advantages of this approach. Applying the proposed method to administrative data on Italian middle schools indicates that school rankings are sensitive to prediction errors, even when extensive controls are added.