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

Bilgen Akmermer Hasan Ayyıldız

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.

Anahtar Kelimeler

: E-Hizmetler, E-eğitim, Yapısal Eşitlik Modeli, Teknoloji Kabul Modeli.

A RESEARCH ON THE DETERMINATION OF THE FACTORS AFFECTING THE ACADEMICIANS' ATTITUDE TOWARDS E-LEARNING SYSTEMS DURING COVID-19 PANDEMIC

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.

Anahtar Kelimeler

E-services, E-learning, Structural Equation Modeling, Technology Acceptance Model.

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.
• Liu, S. Liao, H., & Peng, C. (2005). Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behavior. Issues in Information Systems, 6(2), 175–181.
• Meydan, C. H. & Şeşen, H. (2015). Yapısal eşitlik modellemesi AMOS uygulamaları., Ankara: Seçkin Yayınevi
• Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model, Computer Human Behaviour. 45, 359–374.
• Nagi, K. & Vate-U-Lan, P. (2009). Using emergent technologies for facilitating engaged learning in a virtual learning environment. International Journal of the Computer, the Internet and Management, 17(1), 61-66.
• Ngai E.W.T., Poon, J.K.L. & Chan, Y.H.C. (2007). Empirical examination of the adoption of WebCT using TAM, Computers & Education, 48(2), 250-267.
• Ong, C. S. Lai, J. Y., & Wang, Y. S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information and Management, 41(6), 795–804.
• Ong, C. H. & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior 22(5), 816–829.
• Özdamar, K. (2016). Eğitim, sağlık ve davranış bilimlerinde ölçek ve test geliştirme, yapısal eşitlik modellemesi (1. Baskı). Eskişehir: Nisan Kitabevi.
• 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.
• Pituch, K.A. & Lee, Y.K. (2006). The influence of system characteristics on E-learning use. Computers Education, 47, 222–244.
• Ramirez-Anormaliza, R., Sabate, F. & Guevara-Viejo, F. (2015). Evaluating student acceptance level of e-learning systems, 2015 8th International Conference of Education, Research and Innovation: SevillaVolume: ICERI Proceedings.
• Robertson, J. (1996). Promoting IT competencies with student primary teachers. Journal of Computer Assisted Learning, 12(1), 2–9.
• Roca, J. C. & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace. A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.
• Saade, R. G. (2003). Web-based education information system for enhanced learning, EISL: student assessment. Journal of Information Technology Education, 2, 267–277.
• Salloum, S. A., Alhamad, A.Q., Al-Emran, M., Monem A.A. & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model, IEEE Access, 7, 128445- 128462.
• Salloum, S.A. & Shaalan, K. (2018). Investigating students' acceptance of e-learning system in higher educational environments in the UAE: applying the extended technology acceptance model (TAM) (Thesis for: MSc Informatics), UAE.
• Sanchez-Franco, M. J., Martínez-Lopez, F. J. & Martín-Velicia, F. A. (2009). Exploring the impact of individualism and uncertainty avoidance in web-based electronic learning: an empirical analysis in european higher education. Computers and Education, 52(3), 588–598.
• Sheng, Z., Zhao, J. & Tan., W. (2008). Extending TAM for online learning systems: an intrinsic motivation perspective. Tsinghua Science & Technology, 13(3), 312-317.
• Sumner, M., & Hostetler, D. (1999). Factors influencing the adoption of technology in teaching, The Journal of Computer Information Systems, 40(1), 81-87.
• Şimşek, Ö. F. (2007). Yapısal eşitlik modellemesine giriş temel ilkeler ve Lisrel uygulamaları. Ankara: Ekinoks Yayıncılık.
• Tarhini, A. Hone, K. & Liu, X. (2014). The effects of individual differences on e- Learning users’ behaviour in developing countries: a structural equation model. Computers in Human Behaviours, 41,153–163.
• Tarhini, A., Hone, K., Liu, X. & Tarhini. T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of e-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328, 2017. 

• Thompson, R. L., Higgins, C. A. ve Howell, J. M. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly, 15(1), 124-143.
• Titthasiri, W. (2014). A Strategic decision-making framework for e-learning system: based on strategic planning process and ISO 9126 model. International Journal of Engineering Scıences & Research Technology, 3(7), 588-597.
• Tung, F. C. &Chang, S. C. (2008). An empirical investigation of students' behavioral intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83.
• Valcke, M. (2004). ICT in higher education: An uncomfortable zone for institutes and their policies. Proceedings of the 21. ASCILITE Conference, 20-35.
• Venkatesh, V. &Davis, Fred D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-204.
• 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(4), 1792-1808.
• Webster, J. & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning. Academy of Management Journal, 40(6), 1282–1309.
• Weil, M. M. & Rosen, L. D. (1995). The psychological impact of technology from a global perspective: a study of technological sophistication and technophobia in university students from twenty-three countries. Computers in Human Behavior, 11(1), 95–133.
• Yaşlıoğlu, M. M. (2017). Sosyal Bilimlerde Faktör Analizi ve Geçerlilik: Keşfedici ve Doğrulayıcı Faktör Analizlerinin Kullanılması, İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46, 74-85.
• YÖK, (2020a, 18 Mart). Basın Açıklaması. Erişim adresi https://www.yok.gov.tr/Sayfa lar/Haberler/2020/universitelerde-uygulanacak-uzaktan-egitime-iliskin-aciklama.aspx
• YÖK, (2020b, 13 Mart). Koronavirüs (Covıd-19) Bilgilendirme Notu. 1 Erişim adresi: https://www.yok.gov.tr/Sayfalar/Haberler/2020/coronavirus_bilgilendirme_1.aspx
• YÖK, (2020c, 4 Haziran). YÖK’ten Küresel Salgın İle Mücadele Sürecinde Yeni Düzenlemeler-II. Erişim adresi https://www.yok.gov.tr/ Sayfalar/Haberler/ 2020/küresel-salgin-surecinde-yapisal-duzenlemeler-2.aspx
• Yuen, A. H. K. & Ma, W. W. K. (2008). Exploring teacher acceptance of e‐Learning technology. Asia-Pacific Journal of Teacher Education, 36(3):229-243.