- Available MOX Reports
- Thesis Proposals
- MOX Projects
Masci, C.; Ieva, F.; Paganoni A.m.
EM algorithm for semiparametric multinomial mixed-effects models
Cannistrà,m.; Masci, C.; Ieva, F.; Agasisti, T.; Paganoni, A.m.
Not the magic algorithm: modelling and early-predicting students dropout through machine learning and multilevel approach
Pellagatti, M.; Masci, C.; Ieva, F.; Paganoni A.m.
Generalized Mixed-Effects Random Forest: a flexible approach to predict university student dropout
Masci, C.; Ieva, F.; Agasisti, T.; Paganoni A.m.
Evaluating class and school effects on the joint achievements in different subjects: a bivariate semi-parametric mixed-effects model
Fontana, L.; Masci, C.; Ieva, F.; Paganoni, A.m.
Performing Learning Analytics via Generalized Mixed-Effects Trees
Masci, C.; Paganoni, A.m.; Ieva, F.
Non-parametric mixed-effects models for unsupervised classification of Italian schools
Schiltz, F.; Masci, C.; Agasisti, T.; Horn, D.
Using Machine Learning to Model Interaction Effects in Education: a Graphical Approach.
Masci, C.; Johnes, G.; Agasisti, T.
Student and School Performance in the OECD: a Machine Learning Approach.
Agasisti,t.; Ieva, F.; Masci, C.; Paganoni, A.m.
Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students
Masci, C,; Ieva, F.; Agasisti, T.; Paganoni, A.m.
Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements
The proposed thesis is part of a research project called "Piacenza Orientata" that sees the collaboration of the Department of Mathematics and the Department of Management of Politencico di Milano. The aim of the work is to analyze data about high school students in Piacenza and develop statistical models (also including machine learning techniques) to predict students dropout at the first year of high school, observing collateral information of students and their previous career.