Classification of Students Academic Achievement Using a Random Forest Algorithm Based on Educational Data Mining
Abstract
The categorization of students' academic success presents a significant challenge owing to the effects of various academic, behavioral, and social elements that interact intricately. Precisely determining the categories of student success is crucial for facilitating educational decision-making and early intervention methods. This research sought to create and assess a model for classifying student academic performance utilizing the Random Forest technique within a framework of Educational Data Mining. A supervised machine learning approach was utilized, employing the Student Performance dataset, which comprises 2,392 records of students along with 15 attributes concerning demographic details, study patterns, parental involvement, participation in extracurricular activities, and academic results. The suggested methodology included steps such as data preprocessing, exploratory data analysis, feature selection, splitting the dataset at an 80:20 ratio, training the model, and assessing performance through accuracy, precision, recall, F1-score, analysis of the confusion matrix, evaluation of feature importance, and five-fold cross-validation. The results from the experiments indicated that the Random Forest model reached an accuracy of 90.81% with the testing dataset and exhibited robust classification results across five distinct academic achievement categories. The model performed best in GradeClass 4 and GradeClass 2, whereas lesser performance was noted in the minority classes, likely due to class imbalance. Additionally, the analysis revealed that factors related to study habits and student engagement significantly influenced the classification results. The outcomes suggested that Random Forest is an effective method for classifying multi-class academic performance and could be a dependable resource for informing data-driven educational strategies, student monitoring, and targeted academic interventions.
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