Chiara Masci|Geraint Johnes|Tommaso Agasisti
Student and school performance across countries: A machine learning approach
Politecnico di Milano|Lancaster University|Politecnico di Milano
paper develop apply novel machine learning statistical methods analyse determinants students pisa 2015 test scores nine countries australia canada france germany italy japan spain uk usa aim find student characteristics associated test scores school characteristics associated school valueadded measured school level specific aim approach explore nonlinearities associations covariates test scores well model interactions schoollevel factors affecting results order address issues apply twostage methodology using flexible treebased methods first run multilevel regression trees first stage estimate school valueadded second stage relate estimated school valueadded school level variables means regression trees boosting results show several student school level characteristics significantly associated students achievements marked differences across countries proposed approach allows improved description structurally different educational production functions across countries