Marta Cannistrà
Biographic Data
| ID | 1502479 |
|---|---|
| NAME | Marta Cannistrà |
| GIVEN NAMES | Marta |
| FAMILY NAME | Cannistrà |
| SIGNATURE | CANNISTRÀ M |
| AFFILIATIONS | Politecnico di Milano |
| ORCID | 0000-0001-7631-7790 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Modelling time-to-dropout via shared frailty Cox models. A trade-off between accurate and early predictions
This paper investigates the student dropout phenomenon in a technical Italian university from a time-to-event perspective. Shared frailty Cox time-dependent models are applied to analyse the careers of students enrolled in different engineering programs with the aim of identifying the determinants of student dropout through time, predicting the time to dropout as soon as possible and to observe how the dropout phenomenon varies across time and de…
Predicting dropout in Higher Education across borders
Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students’ dropout based on an integrated comparative study of two universities located in two different European countries: Politecnico di Mil…
The heterogeneity of Covid-19 learning loss across Italian primary and middle schools
This paper investigates the heterogeneous impact of school closures during Covid-19 pandemic in Italy on academic performance across different schools, grades, subjects and groups of students. Our analysis utilises an innovative dataset that combines administrative data on standardised tests in grades 5 and 8 with a specifically-designed survey that collects information about teachers' practices between February and June 2020. Firstly, by employi…
Online or on-campus? Analysing the effects of financial education on student knowledge gain
Early-predicting dropout of university students: An Application of Innovative Multilevel Machine Learning and Statistical Techniques
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university
Early-predicting dropout of university students: An Application of Innovative Multilevel Machine Learning and Statistical Techniques
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university
Predicting dropout in Higher Education across borders
Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students’ dropout based on an integrated comparative study of two universities located in two different European countries: Politecnico di Mil…
Early-predicting dropout of university students: An Application of Innovative Multilevel Machine Learning and Statistical Techniques
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university
The heterogeneity of Covid-19 learning loss across Italian primary and middle schools
This paper investigates the heterogeneous impact of school closures during Covid-19 pandemic in Italy on academic performance across different schools, grades, subjects and groups of students. Our analysis utilises an innovative dataset that combines administrative data on standardised tests in grades 5 and 8 with a specifically-designed survey that collects information about teachers' practices between February and June 2020. Firstly, by employi…
Online or on-campus? Analysing the effects of financial education on student knowledge gain
Modelling time-to-dropout via shared frailty Cox models. A trade-off between accurate and early predictions
This paper investigates the student dropout phenomenon in a technical Italian university from a time-to-event perspective. Shared frailty Cox time-dependent models are applied to analyse the careers of students enrolled in different engineering programs with the aim of identifying the determinants of student dropout through time, predicting the time to dropout as soon as possible and to observe how the dropout phenomenon varies across time and de…
Predicting dropout in Higher Education across borders
Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students’ dropout based on an integrated comparative study of two universities located in two different European countries: Politecnico di Mil…
Psychology (5 works) · Computer Science (4 works) · Economics (3 works) · Mathematics education (3 works) · Artificial Intelligence (2 works) · Higher education (2 works) · Machine learning (2 works) · Mathematics (2 works) · Medicine (2 works) · Online and Blended Learning (2 works)