Özet:
In the context of teaching and learning, evaluating and classifying student achievement
is critical for determining the effectiveness of instructional methods. Categorizing
students’ academic performance into groups such as “passed,” “failed,” “successful,”
and “unsuccessful” provides valuable insights for tracking academic progress and
improving instructional strategies. The use of Machine Learning (ML) models in such
classifications enables more accurate and objective evaluations, particularly when
dealing with large datasets. Therefore, this study aims to examine the accuracy of
various ML models in classifying student performance. ML offers enhanced precision
and objectivity by analyzing large and complex educational datasets. In this study, the
classification accuracies of three machine learning algorithms—Naive Bayes (NB),
Support Vector Machines (SVM), and Random Forest (RF)—were evaluated. The
research compares the performance metrics of these models in predicting students'
academic success and examines the results in detail. As such, the study adopts a
descriptive survey design and has an applied nature. A dataset comprising 1,000
samples and variables such as ethnicity, parental education level, and mathematics
achievement was used. The analyses were conducted using SPSS and R software. The
findings reveal that the Random Forest model achieved the highest classification
accuracy. The integration of ML models in education can contribute to improving
educational quality by predicting student success, identifying risk of failure, and
evaluating the effectiveness of instructional methods and materials.
