Anna Maria Paganoni
Biographic Data
| ID | 1501326 |
|---|---|
| NAME | Anna Maria Paganoni |
| GIVEN NAMES | Anna Maria |
| FAMILY NAME | Paganoni |
| SIGNATURE | PAGANONI A M |
| AFFILIATIONS | Politecnico di Milano |
| ORCID | 0000-0002-8253-3630 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Urban mobility and learning
Despite its crucial role in students' daily lives, commuting time remains an underexplored dimension in higher education research. To address this gap, this study focuses on challenges that students face in urban environments and investigates the impact of commuting time on the Grade Point Average (GPA) of first-year bachelor students of Politecnico di Milano, Italy. This research employs an innovative two-step methodology. In the initial phase, …
Early-predicting dropout of university students
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
Stability over time of the “hospital effect” on 30-day unplanned readmissions
Early-predicting dropout of university students
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
Stability over time of the “hospital effect” on 30-day unplanned readmissions
Early-predicting dropout of university students
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
Urban mobility and learning
Despite its crucial role in students' daily lives, commuting time remains an underexplored dimension in higher education research. To address this gap, this study focuses on challenges that students face in urban environments and investigates the impact of commuting time on the Grade Point Average (GPA) of first-year bachelor students of Politecnico di Milano, Italy. This research employs an innovative two-step methodology. In the initial phase, …
Computer Science (2 works) · Higher education (2 works) · Mathematics (2 works) · Mathematics education (2 works) · Psychology (2 works) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Bachelor (1 work) · Business (1 work) · Causal inference (1 work)