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A novel framework for student performance prediction using optimized ai techniques

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  1. Tez No: 796164
  2. Yazar: ZAINAB ALI MOHAMMED ALRUBAYE
  3. Danışmanlar: DR. ÖĞR. ÜYESİ AYÇA KURNAZ TÜRKBEN
  4. Tez Türü: Yüksek Lisans
  5. Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2022
  8. Dil: İngilizce
  9. Üniversite: Altınbaş Üniversitesi
  10. Enstitü: Lisansüstü Eğitim Enstitüsü
  11. Ana Bilim Dalı: Bilgi Teknolojileri Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 63

Özet

In this study, new deep learning-based system developed to estimate the performance of the students depending on number of features that are vary between datasets. The proposed systems applied deep belief network that is deep learning technique used in various fields in classification and regression problems. Proposed method validated using three datasets are obtained from UCI. These datasets represented various stages of students and various information's about students that are used to validated the proposed method. The traditional classifiers presented low results with several evaluation parameters that are calculated to check the performance of the model and try to determine its weakness compared to other traditional techniques. The traditional classifiers presented results vary between 80%-50% which are very low when compared with our proposed system. The proposed system presented remarkable results which are more than 94% for all datasets that are used in this study.

Özet (Çeviri)

In this study, new deep learning-based system developed to estimate the performance of the students depending on number of features that are vary between datasets. The proposed systems applied deep belief network that is deep learning technique used in various fields in classification and regression problems. Proposed method validated using three datasets are obtained from UCI. These datasets represented various stages of students and various information's about students that are used to validated the proposed method. The traditional classifiers presented low results with several evaluation parameters that are calculated to check the performance of the model and try to determine its weakness compared to other traditional techniques. The traditional classifiers presented results vary between 80%-50% which are very low when compared with our proposed system. The proposed system presented remarkable results which are more than 94% for all datasets that are used in this study.

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