Educational data mining: prediction of students' academic performance using machine learning algorithms

Abstract Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. Such data-driven studies are very important in terms of establishing a learning analysis framework in higher education and contributing to the decision-making processes.

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• 1 This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data.
• 2 The performances of the random forests, nearest neighbour, support vector machines, logistic regression, Naïve Bayes, and k-nearest neighbour algorithms, which are among the machine learning algorithms, were calculated and compared to predict the final exam grades of the students.
• 3 The dataset consisted of the academic achievement grades of 1854 students who took the Turkish Language-I course in a state University in Turkey during the fall semester of 2019–2020.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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