Predicting Graduation and Dropout Rates among Engineering Students Using AI Techniques
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| Title: | Predicting Graduation and Dropout Rates among Engineering Students Using AI Techniques |
|---|---|
| Language: | English |
| Authors: | Ricardo França Santos, Mathis Berthet |
| Source: | Discover Education. 2026 5. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 24 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Predictor Variables, Graduation Rate, Dropout Rate, College Students, Engineering Education, Artificial Intelligence, Algorithms, Foreign Countries, Early Intervention, Dropout Prevention, Mentors, Grade Point Average, Socioeconomic Influences, At Risk Students, Identification, School Holding Power |
| Geographic Terms: | Brazil |
| DOI: | 10.1007/s44217-025-01092-3 |
| ISSN: | 2731-5525 |
| Abstract: | This study introduces an AI model using a random forest algorithm to predict dropout risk among engineering students at the Instituto Politécnico (IPOLI) of the Federal University of Rio de Janeiro. The model provides academic performance, demographic information, and survey responses. Key factors linked to dropout are identified, providing a practical tool for early intervention and prevention. For instance, proactive mentoring could be initiated as early as week two for students flagged by the model, facilitating timely support. Feature importance analysis highlights strong predictors, such as early GPA and socioeconomic conditions, which are correlated rather than causal. The model allows institutions to identify at-risk students early and supports strategies to enhance retention. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1504616 |
| Database: | ERIC |
| Abstract: | This study introduces an AI model using a random forest algorithm to predict dropout risk among engineering students at the Instituto Politécnico (IPOLI) of the Federal University of Rio de Janeiro. The model provides academic performance, demographic information, and survey responses. Key factors linked to dropout are identified, providing a practical tool for early intervention and prevention. For instance, proactive mentoring could be initiated as early as week two for students flagged by the model, facilitating timely support. Feature importance analysis highlights strong predictors, such as early GPA and socioeconomic conditions, which are correlated rather than causal. The model allows institutions to identify at-risk students early and supports strategies to enhance retention. |
|---|---|
| ISSN: | 2731-5525 |
| DOI: | 10.1007/s44217-025-01092-3 |
