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

• Published in: Smart learning environments Vol. 9; no. 1; pp. 1 - 19
• Main Author: Yagci, Mustafa
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 03.03.2022; Springer; Springer Nature B.V; SpringerOpen
• Subjects: Academic Achievement; Accuracy; Algorithms; Artificial Intelligence; Classification; Colleges & universities; Computers and Education; Data Analysis; Data mining; Decision analysis; Decision making; Decision trees; Early warning systems; Education; Educational data mining; Foreign Countries; Grades (Scholastic); Learning analytics; Machine learning; Mathematics; Predicting achievement; Prediction; Students; Support vector machines; Tests; Towards enhancing learning using open educational resources; Undergraduate Students; Undergraduate study; Turkey; Predicting achievement; Educational data mining; Learning analytics; Early warning systems; Machine learning
• DOI: 10.1186/s40561-022-00192-z
• ISSN: 2196-7091

Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. 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. The results show that the proposed model achieved a classification accuracy of 70–75%. The predictions were made using only three types of parameters; midterm exam grades, Department data and Faculty data. 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. Finally, this study presents a contribution to the early prediction of students at high risk of failure and determines the most effective machine learning methods.