Predicting Academic Performance of Students Using Machine Learning Techniques

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dc.contributor.authorZoralioglu, Yildiz
dc.contributor.authorGul, Muhammed Furkan
dc.contributor.authorAzizoglu, Fatma
dc.contributor.authorAzizoglu, Gokhan
dc.contributor.authorToprak, Ahmet Nusret
dc.date.accessioned2024-10-26T17:51:06Z
dc.date.available2024-10-26T17:51:06Z
dc.date.issued2023
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153
dc.description.abstractWith the advancement of information technologies in recent years, it has become common practice to analyze educational data gathered from a variety of sources. Analyzing these education data helps in improving the education system by identifying factors that affect students' progress and assessing their performance in school. Therefore, studies on predicting students' academic performance with high accuracy and extracting meaningful models from vast volumes of education data remain of great importance for researchers. In this study, the academic performance of students is predicted using random forest, decision tree, support vector machines, XGBoost, and logistic regression machine learning algorithms with data from Portuguese schools. In order to increase the prediction performance of the developed model, the imbalance in the dataset is eliminated with the SMOTE technique, and the most important features affecting the performance of the students are selected by using the Recursive Feature Elimination method. The results show that the XGBoost algorithm outperforms the research in the literature with an accuracy value of 97,2%. © 2023 IEEE.
dc.identifier.doi10.1109/ASYU58738.2023.10296648
dc.identifier.isbn979-835030659-0
dc.identifier.scopus2-s2.0-85178300488
dc.identifier.urihttps://doi.org/10.1109/ASYU58738.2023.10296648
dc.identifier.urihttps://hdl.handle.net/20.500.12418/26020
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectmachine learning; recursive feature elimination; smote; student academic performance prediction
dc.titlePredicting Academic Performance of Students Using Machine Learning Techniques
dc.title.alternativeMakine grenmesi Teknikleri ile grencilerin Akademik Başarisinin Tahmini
dc.typeConference Object

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