Author

Kimhong Ann

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Open access Aug 2026

A framework for predicting preservice teacher performance using machine learning and SMOTE-based class imbalance handling

This study determines the most effective model for predicting preservice teachers’ academic outcomes by evaluating multiple machine learning techniques, including Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes across five distinct groups.

Kimhong Ann, Nathaphon Boonnam · Discover Education · 0 citations