Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predicting graduation and dropout rates among engineering students using AI techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

查看全文
  • Ricardo França Santos,
  • Mathis Berthet

摘要

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.