Germain Garcia-Zanabria
Biographic Data
| ID | 9977661 |
|---|---|
| NAME | Germain Garcia-Zanabria |
| GIVEN NAMES | Germain |
| FAMILY NAME | Garcia-Zanabria |
| SIGNATURE | GARCIA-ZANABRIA G |
| AFFILIATIONS | Universidad Católica San Pablo |
| ORCID | 0000-0003-3266-9043 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Con…
The prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and computational techniques based on machine learning. Student Dropout Prediction (SDP) is a challenging problem that can be addressed following various strate…
How Do Curricular Design Changes Impact Computer Science Programs
Computer science is a dynamic field of study that requires constant review and updating of the curricular designs in academic programs—in general, measuring the impact of plan changes has been little explored in the literature. In most cases, it focuses only on structuring its curricula, leaving aside several factors associated with important events or facts such as student dropout, retention, and inclusion. However, these features provide academ…
No prominent works on this page.
How Do Curricular Design Changes Impact Computer Science Programs
Computer science is a dynamic field of study that requires constant review and updating of the curricular designs in academic programs—in general, measuring the impact of plan changes has been little explored in the literature. In most cases, it focuses only on structuring its curricula, leaving aside several factors associated with important events or facts such as student dropout, retention, and inclusion. However, these features provide academ…
Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Con…
The prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and computational techniques based on machine learning. Student Dropout Prediction (SDP) is a challenging problem that can be addressed following various strate…
Computer Science (2 works) · Mathematics education (2 works) · Online Learning and Analytics (2 works) · Political science (2 works) · Psychology (2 works) · Socioeconomic status (2 works) · Sociology (2 works) · Artificial Intelligence (1 work) · Curriculum (1 work) · Demography (1 work)