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

🤖 Plain-English Summary

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.

🔑 Key Findings

• 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.
• 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.
• 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.

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