Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Descriptor
Academic Achievement | 1 |
Accuracy | 1 |
Artificial Intelligence | 1 |
College Students | 1 |
Data Use | 1 |
Dropout Characteristics | 1 |
Dropouts | 1 |
Influences | 1 |
Potential Dropouts | 1 |
Prediction | 1 |
Predictor Variables | 1 |
More ▼ |
Author
Andrea Zanellati | 1 |
Maurizio Gabbrielli | 1 |
Stefano Pio Zingaro | 1 |
Publication Type
Journal Articles | 1 |
Reports - Research | 1 |
Education Level
Higher Education | 1 |
Postsecondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Andrea Zanellati; Stefano Pio Zingaro; Maurizio Gabbrielli – IEEE Transactions on Learning Technologies, 2024
Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…
Descriptors: Dropouts, Dropout Characteristics, Potential Dropouts, Artificial Intelligence