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Using machine learning to predict student outcomes for early intervention and formative assessment.

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This study developed a machine learning model for early prediction of student performance. The model identifies at-risk students, enabling timely interventions to improve academic outcomes.

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Area of Science:

  • Educational Technology
  • Machine Learning in Education
  • Student Performance Prediction

Background:

  • Early prediction of student performance is crucial for timely interventions.
  • Machine learning offers advanced tools for accurate student outcome assessment.
  • Identifying at-risk students early can significantly improve academic success rates.

Purpose of the Study:

  • To develop a machine learning model for predicting student performance.
  • To identify key variables influencing student success.
  • To create an early warning system for academic failure and suggest interventions.

Main Methods:

  • Data collection via student questionnaires.
  • Analysis using four machine learning algorithms: C5.0, CART, Support Vector Machine (SVM), and Random Forest.
  • Evaluation of algorithm effectiveness based on performance accuracy and cross-validation metrics.

Main Results:

  • Random Forest showed consistent cross-validation results.
  • C5.0 achieved higher test set accuracy.
  • CART demonstrated the highest training performance, with performance conflicts analyzed.
  • A new classification model incorporating key influential variables was proposed.

Conclusions:

  • The proposed predictive model serves as a valuable tool for early identification of at-risk students.
  • The model supports formative assessments and enables timely interventions.
  • It aids educators in responding effectively to student needs and promoting equity.