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

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...