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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Monte Carlo optimization for sampling selection in imbalanced data applied to student dropout prediction.

Dianela Herrera1, Nicolás Ángel1, Diego González1

  • 1Departamento de Física, Universidad Católica del Norte, Av. Angamos 0610, Antofagasta 1240000, Chile.

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Summary
This summary is machine-generated.

This study introduces a machine learning tool to predict university student dropout risk. By analyzing student data and using Monte Carlo methods for imbalance, it aims to enable early interventions for at-risk students.

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Area of Science:

  • Educational Data Mining
  • Machine Learning in Education
  • Higher Education Analytics

Background:

  • Student dropout is a global issue affecting academic and professional programs.
  • Existing interventions often prioritize academic performance over other significant factors.
  • Early identification of at-risk students is crucial for effective support.

Purpose of the Study:

  • To develop and evaluate a machine learning tool for early identification of university students at high risk of dropping out.
  • To address data challenges, specifically class imbalance in first-year student data.
  • To facilitate timely and effective preventive interventions for students at risk of attrition.

Main Methods:

  • Utilized a large dataset from Universidad Católica del Norte.
  • Applied and tested various machine learning algorithms for predictive capability.
  • Implemented the Monte Carlo methodology to adjust for class imbalance in first-year data.

Main Results:

  • Machine learning tools demonstrated predictive utility in identifying students at risk of dropout.
  • The Monte Carlo adjustment significantly improved model performance under imbalanced conditions, particularly for first-year dropout.
  • A general level of improvement in model performance was observed across all studied cases.

Conclusions:

  • The developed machine learning tool shows promise for early detection of student dropout risk.
  • Innovative data adjustment techniques like Monte Carlo are effective in handling imbalanced datasets in educational analytics.
  • Early identification enables targeted interventions, potentially improving student retention rates and academic success.