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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
FullText Text:
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  Data: Predicting Graduation and Dropout Rates among Engineering Students Using AI Techniques
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  Data: 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/
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  Data: 10.1007/s44217-025-01092-3
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  Data: 2731-5525
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  Data: 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.
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      – Type: doi
        Value: 10.1007/s44217-025-01092-3
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
    Subjects:
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Graduation Rate
        Type: general
      – SubjectFull: Dropout Rate
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Engineering Education
        Type: general
      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Algorithms
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      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Early Intervention
        Type: general
      – SubjectFull: Dropout Prevention
        Type: general
      – SubjectFull: Mentors
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      – SubjectFull: Grade Point Average
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      – SubjectFull: Socioeconomic Influences
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      – SubjectFull: At Risk Students
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      – SubjectFull: Identification
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      – SubjectFull: School Holding Power
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      – SubjectFull: Brazil
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      – TitleFull: Predicting Graduation and Dropout Rates among Engineering Students Using AI Techniques
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            NameFull: Ricardo França Santos
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            NameFull: Mathis Berthet
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            – D: 01
              M: 12
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              Y: 2026
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