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İnternet destekli öğretimde öğrencilerin başarısını etkileyen faktörlerin istatistiksel yöntemler ve yapay sinir ağları ile analizi

Analyzing factors effect on the success of WEB based learning students' with statistical methods and neural networks

  1. Tez No: 152703
  2. Yazar: HALİL İBRAHİM CEBECİ
  3. Danışmanlar: Y.DOÇ.DR. HARUN REŞİT YAZGAN
  4. Tez Türü: Yüksek Lisans
  5. Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
  6. Anahtar Kelimeler: Uzaktan Öğretim, İnternet Destekli Öğretim, istatistiksel Analiz
  7. Yıl: 2004
  8. Dil: Türkçe
  9. Üniversite: Sakarya Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Endüstri Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 101

Özet

ÖZET

Özet (Çeviri)

ANALYZING FACTORS EFFECT ON THE SUCCESS OF WEB BASED LEARNING STUDENTS' WITH STATISTICAL METHODS AND NEURAL NETWORKS SUMMARY Key words : Distant Learning, Internet Based Learning, Statistical Analysis, Neural Networks The factors which effect to student's success on Internet Based Learning can be classified in four group as personal characteristics, demographic characteristics, learning management system and instructional design. With explicit limits and standards of learning management systems' effects on student's success can be respected as finite. Personal variables are hard values to predict and to be numerical. So, researching the effects of demographic variables and instructional desing would be more effective. This study was prepared to determine how the demographic and instructional design factors change the internet based learning students' successes. After a general introduction, in second chapter short briefings was presented about this literature. In third chapter, distant learning and internet based learning concepts are explained. In fourth chapter, teorical information about statistical analysis methods and neural networks are given. In fifth chapter, learning applications about statistical analysis and neural networks were presented. In last chapter, consequences were discussed and suggestions were presented. After making statistical tests, a meaningful relationship was found between success factors and demographic factors such as age, profession of family man, distinct of the graduation of high school and high school graduation year. A meaningful relationship between success factors and number of brothers/sisters could'nt found. After a regression analysing, a meaningful relationship was found between success factors and instructional design factors before such as length of text, sample and application usage, study questions, short exams and studies, multimedia applications and visual entirety. Furthermore, same values will be processed with multi layer perception model from neural network models. After teaching these values to the network, prediction values were found. In this chapter, a comparative analysis made between regression consequences and values produced by neural network, so two different methods were produced same consequences. XI

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