Araştırma Makalesi
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Uzaktan Eğitim Sisteminin Başarısını Etkileyen Faktörlerin Belirlenmesi

Yıl 2024, Sayı: 59, 249 - 272, 29.03.2024
https://doi.org/10.53444/deubefd.1358870

Öz

Son yıllarda e öğrenme ve uzaktan eğitim uygulamaları eğitim sistemi içerisinde sıklıkla yer almaktadır. Bu anlamda özellikle üniversiteler uzaktan eğitim sistemleri ve alt yapılarını daha etkin hâle getirmektedirler. Buna karşın bireylerin bu sistemlere karşı tutumları günümüzde hala tartışılmaktadır. Çünkü öğrenen ve öğretici pozisyonunda bulunan bireylerin bu sistemlere uyum sağlamaları önemlidir. Bununla birlikte uzaktan eğitim sistemini kullanan bireylerin bu sistemlerden elde edecekleri performans bu sistemlerin kabulünde önemli bir yer tutmaktadır. Bu yüzden bu sistemlerden elde edilecek başarı ve performans bu konuda belirleyici olacaktır. Bu doğrultuda uzaktan eğitim sisteminin başarısını etkileyen faktörler bu araştırmada ele alınmıştır. Teknoloji Kabul Modeli ve Bilgi Sistemleri Başarı Modeli çerçevesinde uzaktan eğitim sistemi kullanan öğrencilerin elde ettikleri bireysel performans ve sistemi kullanma niyetlerini etkileyen faktörler incelenmiştir. Araştırmada bilgi kalitesi, sistem kalitesi, sistem etkileşimi, algılanan eğlence, algılanan memnuniyet, kullanım niyetleri ve bireysel performans değişkenleri ele alınmıştır. Katılımcılardan elde edilen veriler yapısal eşitlik modellemesi altında değerlendirilmiştir. Buna göre bireysel performansı elde etmede bilgi kalitesi ve sistem kalitesi yetersiz kalırken diğer yapılar ise olumlu yönde etkili olmuştur.

Teşekkür

Kolaylıklar Dilerim.

Kaynakça

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Determining The Factors Affecting The Success Of The Distance Education System

Yıl 2024, Sayı: 59, 249 - 272, 29.03.2024
https://doi.org/10.53444/deubefd.1358870

Öz

In recent years, e-learning and distance education applications are frequently included in the education system. In this sense, especially universities make distance education systems and infrastructures more effective. However, individuals' attitudes towards these systems are still debated today. Because it is important that individuals in the position of learner and instructor adapt to these systems. However, the success and performance of individuals using distance education systems have an important place in the acceptance of these systems. Therefore, the performance to be obtained from these systems will be decisive in this regard. This study delves into the factors impacting the success of distance education systems. Utilizing the Technology Acceptance Model and the Information Systems Success Model as frameworks, the research analyzes the factors influencing the individual performance and intention to utilize the system among students engaged in distance education programs. Information quality, system quality, system interaction, perceived enjoyment, perceived satisfaction, usage intentions and individual performance variables were analyzed. The data collected from participants were analyzed using structural equation modeling (SEM). Accordingly, while information quality and system quality were insufficient to achieve individual performance, other structures were positively effective.

Kaynakça

  • Abbad, M. M., Morris, D., & De Nahlik, C. (2009). Looking under the bonnet: Factors affecting student adoption of e-learning systems in Jordan. International Review of Research in Open and Distributed Learning, 10(2).
  • Abbas, T. M., Jones, E., & Hussien, F. M. (2016). Technological factors influencing university tourism and hospitality students’ intention to use e-learning: A comparative analysis of Egypt and the United Kingdom. Journal of Hospitality & Tourism Education, 28(4), 189-201.
  • Ahmed, H. M. S. (2010). Hybrid E‐Learning acceptance model: Learner perceptions. Decision Sciences Journal of Innovative Education, 8(2), 313-346.
  • Ajzen, I. (1980). Understanding attitudes and predictiing social behavior. Englewood cliffs. Alanazi, A. A., Frey, B. B., Niileksela, C., Lee, S. W., Nong, A., & Alharbi, F. (2020). The role of task value and technology satisfaction in student performance in graduate-level online courses. TechTrends, 64(6), 922-930.
  • Al-Azawei, A., Abdullah, A. A., Mohammed, M. K., & Abod, Z. A. (2023). Predicting online learning success based on learners’ perceptions: the ıntegration of the ınformation system success model and the security triangle framework. International Review of Research in Open and Distributed Learning, 24(2), 72-95.
  • Al-Busaidi, K. A., & Al-Shihi, H. (2012). Key factors to instructors’ satisfaction of learning management systems in blended learning. Journal of Computing in Higher Education, 24, 18-39.
  • Al-Debei, M. M., Jalal, D., & Al-Lozi, E. (2013). Measuring web portals success: a respecification and validation of the DeLone and McLean information systems success model. International Journal of Business Information Systems, 14(1), 96-133.
  • Al-Fraihat, D., Joy, M., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in human behavior, 102, 67-86.
  • Anderson, T., & Rivera Vargas, P. (2020). A critical look at educational technology from a distance education perspective. Digital Education Review, 2020, num. 37, p. 208-229.
  • Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Behavior, 66, 388-399.
  • Aparicio, M., Bacao, F., & Oliveira, T. (2016). An e-learning theoretical framework. An e-learning theoretical framework, (1), 292-307.
  • Asabere, N., & Enguah, S. E. (2012). Use of Information & Communication Technology (ICT) in tertiary education in Ghana: A case study of Electronic Learning (E-Learning). International Journal of Information and Communication Technology Research, 2(1), 62-68.
  • Avcı, İ., & Yıldız, E. (2021). Covid-19 pandemi sürecinde uzaktan eğitimi kullanan öğrencilerin memnuniyet ve davranışlarının teknoloji kabul modeli çerçevesinde incelenmesi. Gümüshane University Electronic Journal of the Institute of Social Science/Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 12(3), 814-830.
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  • King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & management, 43(6), 740-755.
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  • Kumar Basak, S., Wotto, M., & Belanger, P. (2018). E-learning, M-learning and D-learning: Conceptual definition and comparative analysis. E-learning and Digital Media, 15(4), 191-216.
  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
  • Lee, Y. C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423–1438.
  • Legris, P.,Ingham, J., &Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191–204.
  • Lin, T. C., & Chen, C. J. (2012). Validating the satisfaction and continuance intention of e-learning systems: Combining TAM and IS success models. International Journal of Distance Education Technologies (IJDET), 10(1), 44-54.
  • Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & education, 54(2), 600-610.
  • Martins, J., Branco, F., Gonçalves, R., Au-Yong-Oliveira, M., Oliveira, T., Naranjo-Zolotov, M., & Cruz-Jesus, F. (2019). Assessing the success behind the use of education management information systems in higher education. Telematics and Informatics, 38, 182-193.
  • Meydan, C. H., & Şeşen, H. (2011). Yapısal eşitlik modellemesi AMOS uygulamaları. Detay Yayıncılık.
  • Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in human behavior, 45, 359-374.
  • Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & management, 38(4), 217-230.
  • Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-Learning, online learning, and distance learning environments: Are they the same?. The Internet and Higher Education, 14(2), 129-135.
  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150-162.
  • Passmore, D. L., & Baker, R. M. (2005). Sampling strategies and power analysis. Research in organizations: Foundations and methods of inquiry, 45-55.
  • Pozón-López, I., Higueras-Castillo, E., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2021). Perceived user satisfaction and intention to use massive open online courses (MOOCs). Journal of Computing in Higher Education, 33, 85-120.
  • Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European journal of information systems, 17, 236-263.
  • Pham, L. T., & Dau, T. K. T. (2022). Online learning readiness and online learning system success in Vietnamese higher education. The International Journal of Information and Learning Technology, 39(2), 147-165.
  • Pituch, K. A. & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.
  • Ramayah, T., & Lee, J. W. C. (2012). System characteristics, satisfaction and e-learning usage: a structural equation model (SEM). Turkish Online Journal of Educational Technology-TOJET, 11(2), 196-206.
  • Ravichandran, T. ve Arun, R. (1999), Total quality management in ınformation systems development: key constructs and relationship, Journal of Management Information Systems, 16(3), s.119- 156.
  • Rotter, J. B. (1967). A new scale for the measurement of interpersonal trust. Journal of personality.
  • Sahin, I., & Shelley, M. (2008). Considering students' perceptions: The distance education student satisfaction model. Journal of Educational Technology & Society, 11(3), 216-223.
  • Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair Jr, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of family business strategy, 5(1), 105-115.
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
  • Seta, H. B., Wati, T., Muliawati, A., & Hidayanto, A. N. (2018). E-learning success model: An extention of DeLone & McLean IS'Success model. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 6(3), 281-291.
  • Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11.
  • Si, J. (2022). Critical e-learning quality factors affecting student satisfaction in a Korean medical school. Korean Journal of Medical Education, 34(2), 107.
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  • Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information systems, 13(1), 24.
  • Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233-244.
  • Türker, E. F. Zorunlu uzaktan eğitime geçiş nedenlerine göre uzaktan eğitim algısının farklılaşması: 6 şubat 2023 depremleri özelinde yükseköğretimdeki uzaktan eğitim algısının incelenmesi. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi, 10(3), 271-300.
  • Türkmen, İ., Sardoğan, B., & Sözen, İ. (2021). Covid-19 sürecinde üniversite öğrencilerinin uzaktan eğitim memnuniyetini etkileyen faktörler üzerine bir araştırma. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(2), 854-875.
  • Özkara, B. Ö., Çivril, H., & Aruğaslan, E. (2022). Üniversite öğrencilerinin uzaktan eğitimi kullanım niyetlerinin utaut bağlamında incelenmesi. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 18(2), 132-153.
  • Urbach, N., Smolnik, S., & Riempp, G. (2010). An empirical investigation of employee portal success. The Journal of Strategic Information Systems, 19(3), 184-206.
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  • Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  • Wang, H. C., & Chiu, Y. F. (2011). Assessing e-learning 2.0 system success. Computers & Education, 57(2), 1790-1800.
  • Wang, Y. S., Wang, H. Y., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(1), 1792–1808.
  • Wang, W. T., & Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774.
  • Wang, H. C., & Chiu, Y. F. (2011). Assessing e-learning 2.0 system success. Computers & Education, 57(2), 1790-1800.
  • Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027-1038.
  • Wang, Y. M., Wei, C. L., Chen, W. J., & Wang, Y. S. (2023). Revisiting the e-learning systems success model in the post-COVID-19 age: The role of monitoring quality. International Journal of Human–Computer Interaction, 1-16.
  • Yakubu, N., & Dasuki, S. (2018). Assessing eLearning systems success in Nigeria: An application of the DeLone and McLean information systems success model. Journal of Information Technology Education: Research, 17, 183-203.
  • Yang, B. (2005). Factor analysis methods. Research in organizations: Foundations and methods of inquiry, (181-199).
  • Yi-Cheng, C., Chun-Yu, C., Yi-Chen, L., & Ron-Chen, Y. (2007). Predicting college student'use of e-learning systems: An attempt to extend technology acceptance model. PACIS 2007 Proceedings, 121.
  • Yörük, T., Nuray, A. K. A. R., & Erdoğan, H. A. N. D. E. (2020). Öğrenme yönetim sistemi kullanımını etkileyen faktörlerin Genişletilmiş Teknoloji Kabul Modeli çerçevesinde Yapısal Eşitlik Modeli ile analizi. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 21(2), 431-449.
  • Yuebo, L., Halili, S. H., & Abdul Razak, R. (2024). Online learning success model for adults in open and distance education in Western China. Plos one, 19(2), e0297515.
  • Zhang, M., Liu, Y., Yan, W., & Zhang, Y. (2017). Users’ continuance intention of virtual learning community services: the moderating role of usage experience. Interactive Learning Environments, 25(6), 685-703.
Toplam 107 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Öğretim Teknolojileri, Eğitim Teknolojisi ve Bilgi İşlem
Bölüm Makaleler
Yazarlar

Abdullah Eren 0000-0003-0391-2825

Yayımlanma Tarihi 29 Mart 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 59

Kaynak Göster

APA Eren, A. (2024). Uzaktan Eğitim Sisteminin Başarısını Etkileyen Faktörlerin Belirlenmesi. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi(59), 249-272. https://doi.org/10.53444/deubefd.1358870