Araştırma Makalesi
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Covid-19 Pandemisi Sürecinde Akademisyenlerin E-Eğitim Sistemlerine Yönelik Tutumlarını Etkileyen Faktörlerin Belirlenmesi Üzerine Bir Araştırma

Yıl 2022, Cilt 23, Sayı 2, 548 - 565, 19.04.2022
https://doi.org/10.37880/cumuiibf.1036688

Öz

2019 yılı Aralık ayı başlarında Çin’de ortaya çıkan ve küresel çapta yayılan Covid-19 pandemisi birçok sosyal ve ekonomik sektörlerde olduğu gibi eğitim sektöründe de acil bir değişim sürecinin yaşanmasına neden olmuştur. Yüz yüze eğitim faaliyetlerine ara vermek zorunda kalan tüm eğitim kurumları gibi üniversiteler de e-eğitim sistemi ile uzaktan eğitim modeline geçmek zorunda kalmıştır. Bu zorunlu süreç üniversitelerde e-eğitim sistemlerinin başarıyla yürütülebilmesi için gerekli araştırma ve geliştirme çalışmalarına yönelik ihtiyacı da ortaya koymuştur. Bu anlamda, e-eğitim sistemlerinin başarıyla yürütülebilmesi ve eğitim hizmetlerinin beklenen kalite ve içerikte sunulabilmesi için gerekli teknolojik altyapının kurulmasının yanında, eğitim sürecini yürüten öğretim üyelerinin yaklaşımlarını anlamak ve sisteme yönelik tutumlarını incelemek de sistemin yürütülebilmesi için önem arz etmektedir. Dolayısıyla, bu araştırma e-eğitim sistemlerini aktif olarak kullanan öğretim üyelerinin sistemin kullanımına yönelik gösterdikleri tutum üzerinde etkili olabilecek faktörleri incelemeyi amaçlamaktadır. Araştırma, Teknoloji Kabul Modeli (TKM) çerçevesinde yürütülmüş, temel model sistemsel, kişisel, mesleki ve kurumsal düzeyde çok yönlü bir bakış açısı sunan farklı faktörler ile genişletilmiştir. Anket deseninde hazırlanan çalışma, bünyesinde bulunan Uzaktan Eğitim ve Uygulama Merkezi ile uzun yıllar tecrübe kazanan Karadeniz Teknik Üniversitesi’nde görev alan 274 öğretim üyesi ile yürütülmüştür. Araştırmanın hem gelecekte e-eğitim hizmetleri üzerine yapılacak benzer çalışmalara referans olması hem de üniversitelerdeki e-eğitim verimliliğinin arttırılması konusunda değerlendirilebilir sonuçlara sahip olması beklenmektedir.

Kaynakça

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  • Al-Busaidi, K.A. & Al-Shihi, H. (2010). Instructors' acceptance of learning management systems: a theoretical framework. IBIMA Publishing, 2-10.
  • Alharbi, S. & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155.
  • Altunısık, R., Coşkun, R., Bayraktaroğlu, S. & Yıldırım, E. (2004), Sosyal bilimlerde arastırma yöntemleri. SPSS Uygulamalı (3. Baskı). Sakarya: Sakarya Kitabevi.
  • Angela, W., Sylvia, C., Handoko, H. & Abdurachman, E. (2018). E-learning acceptance analysis using technology acceptance model (TAM) (case study: Stmik mikroskil). Journal of Theoretical and Applied Information Technology, 96(19), 6292–6305.
  • Ashkanani, A.G.M., (2017). An investigation of the application of the technology acceptance model (TAM) to evaluate instructors’ perspectives on e-learning at Kuwait University (Doktora Tezi, Dublin City University, Kuwait). Erişim Adresi https://www.semanticscholar.org/paper/An-investigation-of-the-application-of-the-Model-to-Alia/a854f999d970d07a796a4de83cb444f06b31b960
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  • Bandura, A. &Wood, R. (1989). Effect of perceived controllability and performance standards on self-regulation of complex decision making. Journal of Personality and Social Psychology, 56(5), 805-14.
  • Bayram, N. (2010), Yapısal eşitlik modellemesine giriş AMOS uygulamaları (1. Baskı). Bursa: Ezgi Kitabevi.
  • Ching-Ter, T., Hajiyev. J. & Su, C. R. (2017).
Examining the students’ behavioral intention to use e-learning in Azerbaijan?
the general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143.
  • Compeau, D.R. & Higgins, C.A. (1995). Computer self-efficacy: development of a measure and initial test, Management Information Systems Quarterly, 19(2), 189-211.
  • Compeau, D., Higgins, C. & Huff, S., (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.
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  • Cross, M. & Adam F. (2007). ICT policies and strategies in higher education in South Africa: national and institutional pathways, Higher Education Policy, 20(1):73-95.
  • Çakır, R. & Solak, E. (2014). Exploring the factors influencing e-learning of Turkish EFL learners through TAM. Turkish Online Journal of Educational Technology, 13(3), 68-76.
  • Çokluk, Ö., Şekercioğlu, G. & Büyüköztürk, Ş. (2016), Sosyal bilimler için çok değişkenli istatistik SPSS ve Lisrel uygulamaları (2. Baskı), Ankara: Pegem Akademi Yayıncılık.
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  • Dillon, A. & Morris, M.G. (1996). User acceptance of information technology: theories and models, Annual Review of Information Science and Technology, 31, 3-32.
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  • Igbaria, M., Greenhaus, J., & Parasuraman, S. (1991). Career orientations of MIS Employees: An Empirical Analysis. Management Information Systems Quarterly, 15(2), 151-169.
  • Imamoglu, S. Z. (2007). An empirical analysis concerning the user acceptance of e-learning, Journal of American Academy of Business, 11(1), 132–137.
  • Islam A.K.M.N., Azad N., Mantymaki M. & Islam S.M.S. (2014). TAM and e-learning adoption: a philosophical scrutiny of TAM, its limitations, and prescriptions for e-learning adoption research, IFIP Advances in Information and Communication Technology, (445), 164–175.
  • İşman, A. (2011). Uzaktan Eğitim, Pagem Akademi Yayınları, 4. Baskı içinde Pegem Akademi, Ankara, 111, 36-37.
  • Kalaycı, Ş. (2016). SPSS uygulamalı çok değişkenli Iistatistik Teknikleri (7. Baskı). Ankara: Asil Yayıncılık.
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  • Kline, Rex B. (2011). Principles and practice of structural equation modeling (3. Baskı). New York: The Guilford Press.
  • Lee, B.C. Yoon J.O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: theories and results. Computers & Education, 53, 1320–1329.
  • Lee, Y., Hsieh, Y. & Hsu. (2011). Adding innovation diffusion theory to technology acceptance model: supporting employees’ intentions to use e-learning systems. Educational Technology & Society, 14 (4), 124-137.
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A RESEARCH ON THE DETERMINATION OF THE FACTORS AFFECTING THE ACADEMICIANS' ATTITUDE TOWARDS E-LEARNING SYSTEMS DURING COVID-19 PANDEMIC

Yıl 2022, Cilt 23, Sayı 2, 548 - 565, 19.04.2022
https://doi.org/10.37880/cumuiibf.1036688

Öz

The Covid-19 pandemic which originated in China in early December 2019 rapidly widespread in almost the world and caused an urgent transformation in the education sector as in many social and economic sectors. Similar to other educational institutions which had to take a break from face-to-face education activities, all universities switched to the distance education model with the e-learning systems. This mandatory process also revealed the need for research and development studies required to successfully execute e-learning systems. In this sense, in addition to establishing the necessary technological infrastructure, it is important to understand the academicians' approaches and examine their attitudes towards the system, for the successful implementation of e-learning systems and the delivery of educational services with the expected quality and content. Therefore, this research aims to examine the factors that may affect the attitudes of academicians who actively use e-learning systems towards the use of the system. The research was conducted within the framework of the Technology Acceptance Model (TAM), and the basic model was expanded with different factors that present a versatile perspective at systemic, personal, occupational, and institutional levels. The study was carried out with 274 academicians working at Karadeniz Technical University, which has years of experience with the Distance Education and Application Centre within its structure. The research is expected to be a reference for other studies to be conducted on e-education services in the future and has evaluable results in increasing e-learning efficiency in universities.

Kaynakça

  • Abdullah, F., Ward, R. & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (peou)and perceived usefulness (pu) of e-portfolios. Computer Human Behaviour, 63, 75–90.
  • Al-Alak, B.A. & Alnawas, I.A.M. (2011). Measuring the acceptance and adoption of e-learning by academic staff. Knowledge Management & E-Learning: An International Journal, 3(2), 201- 221.
  • Al-Busaidi, K.A. & Al-Shihi, H. (2010). Instructors' acceptance of learning management systems: a theoretical framework. IBIMA Publishing, 2-10.
  • Alharbi, S. & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155.
  • Altunısık, R., Coşkun, R., Bayraktaroğlu, S. & Yıldırım, E. (2004), Sosyal bilimlerde arastırma yöntemleri. SPSS Uygulamalı (3. Baskı). Sakarya: Sakarya Kitabevi.
  • Angela, W., Sylvia, C., Handoko, H. & Abdurachman, E. (2018). E-learning acceptance analysis using technology acceptance model (TAM) (case study: Stmik mikroskil). Journal of Theoretical and Applied Information Technology, 96(19), 6292–6305.
  • Ashkanani, A.G.M., (2017). An investigation of the application of the technology acceptance model (TAM) to evaluate instructors’ perspectives on e-learning at Kuwait University (Doktora Tezi, Dublin City University, Kuwait). Erişim Adresi https://www.semanticscholar.org/paper/An-investigation-of-the-application-of-the-Model-to-Alia/a854f999d970d07a796a4de83cb444f06b31b960
  • Bagozzi, R. P. & Yi, Y. (1988), “On the Evaluation of Structural Equation Models”, Journal of the Academy of Marketing Science, 16(1), 74–94.
  • Ball, D.M. & Levy, Y. (2008). Emerging educational technology: assessing the factors that influence instructors' acceptance in information systems and other classrooms. Journal of Information System Education, 19, 431-444.
  • Bandura, A. &Wood, R. (1989). Effect of perceived controllability and performance standards on self-regulation of complex decision making. Journal of Personality and Social Psychology, 56(5), 805-14.
  • Bayram, N. (2010), Yapısal eşitlik modellemesine giriş AMOS uygulamaları (1. Baskı). Bursa: Ezgi Kitabevi.
  • Ching-Ter, T., Hajiyev. J. & Su, C. R. (2017).
Examining the students’ behavioral intention to use e-learning in Azerbaijan?
the general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143.
  • Compeau, D.R. & Higgins, C.A. (1995). Computer self-efficacy: development of a measure and initial test, Management Information Systems Quarterly, 19(2), 189-211.
  • Compeau, D., Higgins, C. & Huff, S., (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.
  • Cross, J. (2004). An informal history of e-learning. On the Horizon, 12(3):103-110.
  • Cross, M. & Adam F. (2007). ICT policies and strategies in higher education in South Africa: national and institutional pathways, Higher Education Policy, 20(1):73-95.
  • Çakır, R. & Solak, E. (2014). Exploring the factors influencing e-learning of Turkish EFL learners through TAM. Turkish Online Journal of Educational Technology, 13(3), 68-76.
  • Çokluk, Ö., Şekercioğlu, G. & Büyüköztürk, Ş. (2016), Sosyal bilimler için çok değişkenli istatistik SPSS ve Lisrel uygulamaları (2. Baskı), Ankara: Pegem Akademi Yayıncılık.
  • Davis, F.D. (1989). Perceived usefullness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319-339.
  • Dillon, A. & Morris, M.G. (1996). User acceptance of information technology: theories and models, Annual Review of Information Science and Technology, 31, 3-32.
  • El-Tartoussi, I. (2009). Networked readiness in the United Arab Emirates. 2009 2nd Annual Forum on e-Learning Excellence in the Middle East, Dubai, UAE.
  • Erkorkmaz, Ü., Etikan, İ., Demir, O., Özdamar, K. & Sanisoğlu, S.Y. (2013). Doğrulayıcı Faktör Analizi ve Uyum Indeksleri. Türkiye Klinikleri, Journal of Medical Sciences, 33(1), 210-223.
  • Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. & Tatham, R.L. (2010). Multivariate data analysis: a global perspective (7th ed). Hoboken, NJ, USA: Pearson Education Inc.
  • Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2013). Multivariate data analysis: pearson new international edition. (7th ed). Hoboken, NJ, USA: Pearson Education Inc.
  • Hu, P.J., Clark, T.H.K. & Ma, W.W. (2003). Examining technology acceptance by school teachers: a longitudinal study, Information & Management, 41,227–241.
  • Hussein, H. (2011). Attitudes of Saudi Universities faculty members towards using e-learning management system (JUSUR), The Turkish Online Journal of Education Technology, 10(2), 1-11.
  • Igbaria, M., Greenhaus, J., & Parasuraman, S. (1991). Career orientations of MIS Employees: An Empirical Analysis. Management Information Systems Quarterly, 15(2), 151-169.
  • Imamoglu, S. Z. (2007). An empirical analysis concerning the user acceptance of e-learning, Journal of American Academy of Business, 11(1), 132–137.
  • Islam A.K.M.N., Azad N., Mantymaki M. & Islam S.M.S. (2014). TAM and e-learning adoption: a philosophical scrutiny of TAM, its limitations, and prescriptions for e-learning adoption research, IFIP Advances in Information and Communication Technology, (445), 164–175.
  • İşman, A. (2011). Uzaktan Eğitim, Pagem Akademi Yayınları, 4. Baskı içinde Pegem Akademi, Ankara, 111, 36-37.
  • Kalaycı, Ş. (2016). SPSS uygulamalı çok değişkenli Iistatistik Teknikleri (7. Baskı). Ankara: Asil Yayıncılık.
  • Karagöz, Y. (2016). SPSS 23 ve AMOS 23 uygulamalı Iistatistiksel analizler (1.Baskı). Ankara: Nobel Akademik Yayıncılık.
  • Khan, B. H. (2005). Managing e-learning: design, delivery, implementation and evaluation, Hershey, PA: Information Science Publishing.
  • Kim, B. & Park, M.J. (2017). Effect of personal factors to use ICTs on e-learning adoption: comparison between learner and instructor in developing countries. Journal of Information Technology for Development, 24(4), 706-732.
  • Kline, Rex B. (2011). Principles and practice of structural equation modeling (3. Baskı). New York: The Guilford Press.
  • Lee, B.C. Yoon J.O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: theories and results. Computers & Education, 53, 1320–1329.
  • Lee, Y., Hsieh, Y. & Hsu. (2011). Adding innovation diffusion theory to technology acceptance model: supporting employees’ intentions to use e-learning systems. Educational Technology & Society, 14 (4), 124-137.
  • Lei, S. A. & Gupta, R. K. (2010). College distance education courses: evaluating benefits and costs from institutional, faculty and students’ perspectives. Education, 130(4), 616–631.
  • Li, H. & Masters, J. (2009). ELearning and knowledge management in the early years: Where are we and where should we go. knowledge management and e-learning: An International Journal, 1(4), 245-250.
  • Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the blackboard system. Computers and Education, 51(2), 864–873.
  • Lin, H. F. (2007). Measuring online learning systems success: applying the updated DeLone and McLean model. Cyberpsychology & behavior, 10(6), 817-820.
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Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Makaleler
Yazarlar

Bilgen AKMERMER (Sorumlu Yazar)
KARADENİZ TEKNİK ÜNİVERSİTESİ
0000-0003-4201-5254
Türkiye


Hasan AYYILDIZ
KARADENİZ TEKNİK ÜNİVERSİTESİ
0000-0003-1954-6719
Türkiye

Erken Görünüm Tarihi 15 Nisan 2022
Yayımlanma Tarihi 19 Nisan 2022
Başvuru Tarihi 15 Aralık 2021
Kabul Tarihi 6 Şubat 2022
Yayınlandığı Sayı Yıl 2022, Cilt 23, Sayı 2

Kaynak Göster

APA Akmermer, B. & Ayyıldız, H. (2022). Covid-19 Pandemisi Sürecinde Akademisyenlerin E-Eğitim Sistemlerine Yönelik Tutumlarını Etkileyen Faktörlerin Belirlenmesi Üzerine Bir Araştırma . Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi , 23 (2) , 548-565 . DOI: 10.37880/cumuiibf.1036688

İrtibat
Sivas Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi,
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58140 Kampüs-SİVAS.

Tel: 0346 2191010-1710

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