Enhancing Student Dropout Prediction Using Chi-Square, SMOTE-ENN, and Hyperparameter Tuning of Random Forest

Reducing dropout rates is crucial for enhancing human capital and education standards. Existing methods, such as Random Forest with Chi-Square and SMOTE-ENN, effectively addressed class imbalance and improved prediction accuracy for dropout data. However, there is still a research gap in achieving optimal model performance. This study addresses the gap by incorporating hyperparameter tuning alongside ChiSquare for feature selection and SMOTE-ENN for handling class imbalance. The dataset was segmented into training and evaluation subsets through the implementation of 10 -fold cross-validation. The testing was conducted with seven variations, namely building and implementing a Random Forest model using the default parameters from the Weka tool and applying six different hyperparameter tuning techniques. The results showed that Hyperband, along with other techniques like TPE, RandomSearch, and BO-TPE, led to substantial improvements in model accuracy, precision, and F-measure, and achieved perfect AUC scores. However, BO-GP and Nevergrad did not improve model performance. These findings suggest that the combination of SMOTE-ENN, Chi-Square, and hyperparameter tuning can enhance the effectiveness of dropout prediction models, with potentially positive implications for early intervention strategies in educational institutions.