Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 1 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Artificial Intelligence | 1 |
| At Risk Students | 1 |
| Classification | 1 |
| College Students | 1 |
| Data Analysis | 1 |
| Decision Making | 1 |
| Foreign Countries | 1 |
| Grades (Scholastic) | 1 |
| Mathematical Formulas | 1 |
| Models | 1 |
| Potential Dropouts | 1 |
| More ▼ | |
Source
| European Journal of Higher… | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
Location
| Germany | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Kemper, Lorenz; Vorhoff, Gerrit; Wigger, Berthold U. – European Journal of Higher Education, 2020
We perform two approaches of machine learning, logistic regressions and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e. data available at all universities without the need of specific collection. Therefore, we propose a methodical approach…
Descriptors: Foreign Countries, Predictor Variables, Potential Dropouts, School Holding Power

Peer reviewed
Direct link
