SavedAdd your journal

Journals / Bartın Üniversitesi Eğitim Fakültesi Dergisi / 2020 /

Development and Validation of the Cloud Technologies Usage in Education Scale

Agah Tuğrul KORUCU
Aydin KARAKOCA
Pages
69–82
Publication Date
2020-02-05
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

For the second sample, it was found that the model formed by the two-factor structure of the scale was appropriate according to the fit indices obtained from the confirmatory factor analysis results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Öz In this research, it is aimed to develop a scale for the use of cloud technologies in education.
📊
Key Findings & Data:The sample group of the study consists of 415 preservice teachers who are studying at universities in Konya.
💡
Conclusion & Impact:For the second sample, it was found that the model formed by the two-factor structure of the scale was appropriate according to the fit indices obtained from the confirmatory factor analysis results.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Anahtar Kelimeler Cloud Technologies · Scale Development · Technology Usage

Özet

Öz In this research, it is aimed to develop a scale for the use of cloud technologies in education. The sample group of the study consists of 415 preservice teachers who are studying at universities in Konya. For the validity and reliability analyses of the scale, the sample group consisting of 415 units was randomly allocated (=208 and =207) sub-samples, the first sample was used for explanatory factor analysis and the second sample was used for confirmatory factor analysis. As a result of the explanatory factor analysis of the data obtained from the first group, 6-item scale consists of motivation and interaction sub-dimensions. Interaction dimension of total variability alone explains 35.89% and motivation dimension explains 33.56%. Factor loads for the sub-dimensions ranged between 0.74 and 0.83. The internal consistency coefficient was 0.83 for Cronbach alpha, 0.77 for motivation subscale and 0.79 for interaction subscale. For the second sample, it was found that the model formed by the two-factor structure of the scale was appropriate according to the fit indices obtained from the confirmatory factor analysis results. As a result, Cloud Technologies Usage scale was found to be a valid and reliable measurement tool.

🔖

Cite This Article

KORUCU, A. T., KARAKOCA, A. (2020). Development and Validation of the Cloud Technologies Usage in Education Scale. Bartın Üniversitesi Eğitim Fakültesi Dergisi, 69–82. https://ajindex.com/articles/50147

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Akgül, A. (2005). Tıbbi Araştırmalarda İstatistiksel Analiz Teknikleri. Spss Uygulamaları. Ankara. ISBN: 975-96359-2-5.
  2. Alpar, R. (1998). İstatistik ve Spor Bilimleri. Ankara:
  3. Bağırgan Yayınevi.
  4. Al-Zoube, M. (2009). E-Learning on the Cloud. Int. Arab J. e-Technol., 1(2), 58-64.
  5. Arpaci, I. (2016). Understanding and predicting students' intention to use mobile cloud storage services. Computers in Human Behavior, 58, 150-157.
  6. Aydın, B. (2003). Bilgi toplumu oluşumunda bireylerin yetiştirilmesi ve matematik öğretimi. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 14(14), 183-190.
  7. Barrett, P. (2007). Structural equation modelling: Adju¬dging model fit. Personality And Individual Diffe-rences, 42(5), 815-824.
  8. Batibay, E. F. (2019). Web 2.0 Uygulamalarinin Türkçe Dersinde Motivasyona Ve Başariya Etkisi: Kahoot Örneği, Master's thesis, Eğitim Bilimleri Enstitüsü). Beldarrain, Y. (2006). Distance education trends: Integrating new technologies to foster student interaction and collaboration. Distance education, 27(2), 139-153.
  9. Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach's alpha. Bmj, 314(7080), 572. Bouyer, A., & Arasteh, B. (2014). The necessity of using cloud computing in educational system. Procedia-Social and Behavioral Sciences, 143, 581-585.
  10. Bower, M., Hedberg, J., & Kuswara, A. (2009). “Conceptualising Web 2.0 Enabled Learning Designs. Same Places, Different Spaces.” Proceedings Ascilite 2009, Auckland. Accessed June 24, 2019. http://www.ascilite.org.au/conferences/auckland09/procs/bower.pdf.
  11. Burda, D., & Teuteberg, F. (2016). Exploring consumer preferences in cloud archiving–a student's perspective. Behaviour & Information Technology, 35(2), 89-105.
  12. Büyüköztürk, Ş. (2002). Faktör Analizi Temel Kavramlar ve Ölçek Geliştirmede Kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, 32(470-48).
  13. Büyüköztürk, Ş. (2005) Veri Analizi El Kitabı (5. Basım). Ankara:
  14. PegemA Yayıncılık. Büyüköztürk, Ş. (2011). Sosyal bilimler için veri analizi el kitabı. Ankara:
  15. Pegem Akademi. Çelik, H. E., & Yılmaz, V. (2016). LISREL 9.1 ile yapısal eşitlik modellemesi: Temel kavramlar-uygulamalar-programlama. Anı Yayıncılık. Dursun, Ö. Ö., & Aydın, C. H. (2011). İletişimci Biçimleri Ölçeğinin Türkçeye Çevirisi, Uyarlanması, Geçerlik Ve Güvenirliğinin Sağlanması. Eskişehir Osmangazi Üniversitesi İibf Dergisi, 6(2), 263 286. Elamir, A. M., Jailani, N., & Bakar, M. A. (2013). Framework and architecture for programming education environment as a cloud computing service. Pro