Improving student success rate: an optimisation model for language proficiency prediction based on a random forest classifier

Key Points

• The aim is to develop a predictive model for language proficiency using advanced statistical methods.
• Used a random forest classifier to analyze language proficiency data.
• Implemented an optimisation model to enhance prediction accuracy.
• Compared performance metrics to validate the model's effectiveness.
• The random forest model demonstrated a high level of accuracy in predicting language proficiency.
• Improvements in predictions correlated with better student success rates.

Abstract