Predicting Academic Failure: Estimating Semester Grade Point Average with Data Mining Methods
Yazarlar (2)
Prof. Dr. Mustafa YAĞCI Türkiye
Yusuf Ziya Olpak Kırşehir Ahi Evran Üniversitesi
| Makale Türü |
Özgün Makale
(Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale)
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| Makale Dili | – | Basım Tarihi | 07-2021 |
| Makale Linki | https://www.researchsquare.com/article/rs-698647/latest | ||
| UAK Araştırma Alanları |
Bilgisayar ve Öğretim Teknolojileri Eğitimi
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| Özet |
| This study proposes a new model to analyze the grade point averages (GPAs) in the previous semester using data mining algorithms and to predict the final GPAs that students may receive in the following semesters in three gradually expanding categories (department, faculty, and university). The performances of the Random Forest, Linear Regression, Support Vector Machines, and k-Nearest Neighbors algorithms, which are among the data mining algorithms, were calculated and compared to estimate the GPAs of the students at the end of the semester. This study focused on three parameters. The first was to predict academic performance with a single independent variable. The second was to compare the performance indicators of four algorithms. The third was to compare the predictions made in three different categories. All algorithms applied correctly classified the samples at rates varying between 92% and 94%. The proposed model correctly estimated students’ grade point averages at the end of the semester with an average deviation of 0.28 points over a 4 with a single variable. Students with a high risk of failure can be determined in advance by estimating their final grade point averages. |
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