Educational data mining: prediction of students’ academic performance using machine learning algorithms
Yazarlar (1)
Prof. Dr. Mustafa YAĞCI Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(ESCI dergilerinde yayınlanan tam makale)
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| Dergi Adı | Smart Learning Environments | ||
| Dergi ISSN | 2196-7091 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | ESCI, SCOPUS, EBSCO, DOAJ | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2022 |
| Cilt / Sayı / Sayfa | 9 / 1 / 1–19 | DOI | 10.1186/s40561-022-00192-z |
| Makale Linki | https://slejournal.springeropen.com/articles/10.1186/s40561-022-00192-z | ||
| Özet |
| 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 … |
| Anahtar Kelimeler |
| Early warning systems | Educational data mining | Learning analytics | Machine learning | Predicting achievement |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 960 |
| Scopus | 592 |
| Web of Science | 264 |