Araştırma Makalesi
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Üniversite Öğrencilerinin Uzaktan Eğitimi Kullanım Niyetlerinin UTAUT Bağlamında İncelenmesi

Yıl 2022, Cilt 18, Sayı 2, 132 - 153, 22.08.2022
https://doi.org/10.17860/mersinefd.991686

Öz

Uzaktan eğitim sistemlerinin uygulama aşamasında kabul görmesindeki etkenlerin başında kullanıcıların bu teknolojiyi kabul edip kullanması gelmektedir. Bu çalışmada, birleştirilmiş teknoloji kabul ve kullanımı kuramı (UTAUT) kapsamında geliştirilen araştırma modeli doğrultusunda örgün öğretimde kayıtlı üniversite öğrencilerinin uzaktan eğitim sistemini gelecekte kullanma niyetlerine etki eden faktörler ve bu faktörlerin davranışsal niyet üzerinde cinsiyetin etkisi araştırılmış ve çalışmada ilişkisel tarama modeli kullanılmıştır. Öğrencilerin uzaktan eğitimi kullanmalarına yönelik niyetlerini belirlemek amacıyla alan yazın taraması sonucunda bir ölçek hazırlanmış ve veriler 649 önlisans ve lisans öğrencisinden toplanmıştır. Bu veriler doğrultusunda araştırma modelindeki yapılar arasındaki ilişkiler Kısmi En Küçük Kareler Yapısal Eşitlik Modeli (KEKK-YEM) yaklaşımı ile test edilmiştir. Çalışma bulguları, performans beklentisinin ve sosyal etkinin öğrencilerin uzaktan eğitimi gelecekte kullanımına yönelik davranışsal niyeti üzerinde pozitif bir etkisi olduğunu göstermektedir. Ayrıca sosyal etki ile davranışsal niyet arasında cinsiyet grupları açısından anlamlı bir farklılık olduğu, kadınların erkeklere oranla sosyal etkiye daha fazla önem verdikleri görülmüştür.

Kaynakça

  • Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: an extended framework and A mixed-method study. Education and Information Technologies, 24(3), 2127-2144. Retrived from https://link.springer.com/article/10.1007/s10639-019-09865-8
  • Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673-174686. doi: 10.1109/ACCESS.2019.2957206
  • Alshehri, A., Rutter, M, & Smith, S. (2020). The effects of UTAUT and usability qualities on students’ use of learning management systems in Saudi tertiary education. Journal of Information Technology Education: Research, 19, 891-930. Retrived from https://www.learntechlib.org/p/218293/.
  • Altalhi, M. (2020). Toward a model for acceptance of MOOCs in higher education: the modified UTAUT model for Saudi Arabia. Education and Information Technologies, 1-17. https://doi.org/10.1007/s10639-020-10317-x.
  • Arif, M., Ameen, K. & Rafiq, M. (2018). Factors affecting student use of Web-based services: Application of UTAUT in the Pakistani context. The Electronic Library, 36(3), 518-534. Retrived from https://www.emerald.com/insight/content/doi/10.1108/EL-06-2016-0129/full/html?af=R
  • Balkaya, S., & Akkucuk, U. (2021). Adoption and use of learning management systems in education: the role of playfulness and self-management. Sustainability, 13(3), 1127. doi.org/10.3390/su13031127
  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173. Retrived from https://psycnet.apa.org/buy/1987-13085-001
  • Beldarrain, Y. (2006.) Distance education trends: Integrating new technologies to foster student interaction and collaboration. Distance Education, 27(2), 139-153. doi.org/10.1080/01587910600789498
  • Bonk, C. J. (2020). Pandemic ponderings, 30 years to today: synchronous signals, saviors, or survivors?. Distance Education, 41(4), 589-599. doi.org/10.1080/01587919.2020.1821610
  • Chaka, J. G., & Govender, I. (2017). Students’ perceptions and readiness towards mobile learning in colleges of education: a Nigerian perspective. South African Journal of Education, 37(1), 1-12. doi. 10.15700/saje.v37n1a1282
  • Chen, P. Y., & Hwang, G. J. (2019). An empirical examination of the effect of self-regulation and the Unified Theory of Acceptance and Use of Technology (UTAUT) factors on the online learning behavioural intention of college students. Asia Pacific Journal of Education, 39(1), 79-95. doi.org/10.1080/02188791.2019.1575184
  • Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. In G. A. Marcoulides (Ed.), Modern methods for business research içinde (ss. 295–336). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.
  • Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. doi.org/10.1016/j.im.2008.02.003
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272-281. doi.org/10.1016/j.chb.2015.03.022
  • Diehl, W. (2020). Opportunities and change amidst debate, confusion, and challenges in education. American Journal of Distance Education, 34(4), 259-259. doi.org/10.1080/08923647.2020.1853424
  • Fianu, E., Blewett, C., & Ampong, G. O. (2020). Toward the development of a model of student usage of MOOCs. Education + Training, 62(5), 521–541. Retrived from https://www.emerald.com/insight/content/doi/10.1108/ET-11-2019-0262/full/html
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi.org/10.1177/002224378101800104
  • Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review.
  • Hair Jr. F., Hult, G.T.M., Ringle, C.M. & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling. Sage, Thousand Oaks, CA.
  • Hair, J. F. Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. doi.org/10.2753/MTP1069-6679190202
  • Henseler J.,& Fassott G. (2010) Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures. In V. Esposito Vinzi, W. W. Chin, J. Henseler & H. Wang (Eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics (pp. 713-735). Springer, Berlin, Heidelberg.
  • Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. in new challenges to international marketing. Bingley: Emerald Group Publishing Limited.
  • Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91-112. doi.org/10.1080/07421222.1999.11518247
  • Karasar, N. (2009). Bilimsel araştırma yöntemi (19. Baskı). Ankara: Nobel
  • Kharma, Q. (2019). Investigating students ‘acceptance of online courses at Al-Ahliyya Amman University. International Journal of Advanced Computer Science and Applications, 10(7), 202-208.
  • Lakhal, S., & Khechine, H. (2021). Technological factors of students’ persistence in online courses in higher education: The moderating role of gender, age and prior online course experience. Education and Information Technologies, 1-27. https://doi.org/10.1007/s10639-020-10407-w
  • Lee, L., Petter, S., Fayard, D., & Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305-328. doi.org/10.1016/j.accinf.2011.05.002
  • Mahande, R. D., & Malago, J. D. (2019). An e-learning acceptance evaluation through UTAUT model in a postgraduate program. Journal of Educators Online, 16(2), n2. Retrieved from https://eric.ed.gov/?id=EJ1223779
  • Mulik, S., Srivastava, M., & Yajnik, N. (2018). Extending UTAUT model to examine MOOC adoption. NMIMS Management Review, XXXVI(2), 26-44.
  • Naidu, S. (2020) It is the worst—and the best—of times!. Distance Education, 41(4), 425-428. doi.org/10.1080/01587919.2020.1825929
  • Odegbesan, O. A., Ayo, C., Oni, A. A., Tomilayo, F. A., Gift, O. C., & Nnaemeka, E. U. (2019, August). The prospects of adopting e-learning in the Nigerian education system: a case study of Covenant University. In Journal of Physics: Conference Series (Vol. 1299, No. 1, p. 012058). IOP Publishing.
  • Putri, V. Q., Shihab, M. R., & Hidayanto, A. N. (2019, October). Does inertia effect e-learning system acceptance among university lecturers? Insights from Sriwijaya University. In 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS) (pp. 307-312). IEEE.
  • Radovan, M., & Kristl, N. (2017). Acceptance of technology and ıts ımpact on teachers' activities in virtual classroom: ıntegrating UTAUT and CoI into a Combined Model. Turkish Online Journal of Educational Technology-TOJET, 16(3), 11-22. Retrieved from https://files.eric.ed.gov/fulltext/EJ1152624.pdf
  • Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2020). Social isolation and acceptance of the learning management system (LMS) in the time of covıd-19 pandemic: an expansion of the UTAUT model. Journal of Educational Computing Research, 59(2),183-208. doi.org/10.1177/0735633120960421
  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.
  • Salloum, S. A., & Shaalan, K. (2018, September). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In International Conference on Advanced Intelligent Systems and Informatics (pp. 469-480). Springer, Cham.
  • Samsudeen, S.N., & Mohamed, R. (2019), University students’ intention to use e-learning systems: a study of higher educational institutions in Sri Lanka, Interactive Technology and Smart Education, 16(3), 219-238. Retrieved from https://www.emerald.com/insight/content/doi/10.1108/ITSE-11-2018-0092/full/html
  • Sattari, A., Abdekhoda, M., & Zarea Gavgani, V. (2017). Determinant factors affecting the web–based training acceptance by health students, applying UTAUT model. International Journal of Emerging Technologies in Learning. 12(10), 112-126.doi.org/10.3391/ijet.v12i10.7258
  • Shaqrah, A. A. (2015). Explain the behavior intention to use e-learning technologies: A unified theory of acceptance and use of technology perspective. International Journal of Web-Based Learning and Teaching Technologies, 10(4), 19-32. doi.10.4018/IJWLTT.2015100102
  • Šumak, B., Polancic, G., & Hericko, M. (2010, February). An empirical study of virtual learning environment adoption using UTAUT. In 2010 Second international conference on mobile, hybrid, and on-line learning (pp. 17-22). IEEE.
  • Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 1-12. doi. 2158244013503837.
  • Telli, S. & Altun, D. (2020). Coronavirüs ve çevrimiçi (online) eğitimin önlenemeyen yükselişi. Üniversite Araştırmaları Dergisi, 3(1), 25-34. doi.org/10.32329/uad.711110
  • Thowfeek, M.H. & Jaafar, A. (2013) An investigation of the factors that influence students’ intention to adopt e-learning. In H.B. Zaman, P. Robinson, P. Olivier, T.K. Shih ve S. Velastin (Eds.) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science: Springer, Cham.
  • Umrani-Khan, F., & Iyer, S. (2009, July). ELAM: A model for acceptance and use of e-learning by teachers and students. In Proceedings of the International Conference on e-Learning (pp. 475-485). Institute of Technology Bombay, Mumbai, India UNESCO, (2021b). UNESCO figures show two thirds of an academic year lost on average worldwide due to Covid-19 school closures. (Erişim: 13.02.2021), https://en.unesco.org/news/unesco-figures-show-two-thirds-academic-year-lost-average-worldwide-due-covid-19-school
  • UNESCO, (2021a). Education: From disruption to recovery. (Erişim: 13.02.2021), https://en.unesco.org/covid19/educationresponse
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi.org/10.2307/30036540
  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of ınformation technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. doi.org/10.2307/41410412
  • Wang, M. H. (2016). Factors influencing usage of e-learning systems in Taiwan's public sector: applying the UTAUT Model. Advances in Management and Applied Economics, 6(6), 63-82. Retrieved from http://www.scienpress.com/Upload/AMAE/Vol%206_6_5.pdf
  • Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492–502. doi.org/10.1177/0266666918765907
  • Yükseköğretim Kurulu, (2020). Pandemi Günlerinde Türk Yükseköğretimi. (Erişim: 13.02.2021), https://covid19.yok.gov.tr/Sayfalar/HaberDuyuru/pandemi-gunlerinde-turk-yuksekogretimi.aspx

Investigation of University Students' Intention to Use Distance Education From the Perspectives of UTAUT

Yıl 2022, Cilt 18, Sayı 2, 132 - 153, 22.08.2022
https://doi.org/10.17860/mersinefd.991686

Öz

One of the main factors in the acceptance of distance education systems is that users accept and use this technology. In this study, we investigate the factors affecting the intention of university students enrolled in formal education to use the the distance education system in the future and the effect of gender on behavioral intention, by using the research model developed within the scope of the Unified Theory of Acceptance and Use of Technology (UTAUT). In this study, a correlational survey model was used. Upon literature review, a scale was prepared in order to determine the intentions of students to use distance education and the data were collected from a total of 649 associate and undergraduate students through the scale. The correlation between the structures in the research model was tested with the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The results of the study showed that performance expectation and social impact had a positive effect on students' behavioral intention to use distance education in the future. In addition, it was observed that there was a significant difference between social influence and behavioral intention between gender groups, and women attach more importance to social impact than men.

Kaynakça

  • Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: an extended framework and A mixed-method study. Education and Information Technologies, 24(3), 2127-2144. Retrived from https://link.springer.com/article/10.1007/s10639-019-09865-8
  • Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673-174686. doi: 10.1109/ACCESS.2019.2957206
  • Alshehri, A., Rutter, M, & Smith, S. (2020). The effects of UTAUT and usability qualities on students’ use of learning management systems in Saudi tertiary education. Journal of Information Technology Education: Research, 19, 891-930. Retrived from https://www.learntechlib.org/p/218293/.
  • Altalhi, M. (2020). Toward a model for acceptance of MOOCs in higher education: the modified UTAUT model for Saudi Arabia. Education and Information Technologies, 1-17. https://doi.org/10.1007/s10639-020-10317-x.
  • Arif, M., Ameen, K. & Rafiq, M. (2018). Factors affecting student use of Web-based services: Application of UTAUT in the Pakistani context. The Electronic Library, 36(3), 518-534. Retrived from https://www.emerald.com/insight/content/doi/10.1108/EL-06-2016-0129/full/html?af=R
  • Balkaya, S., & Akkucuk, U. (2021). Adoption and use of learning management systems in education: the role of playfulness and self-management. Sustainability, 13(3), 1127. doi.org/10.3390/su13031127
  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173. Retrived from https://psycnet.apa.org/buy/1987-13085-001
  • Beldarrain, Y. (2006.) Distance education trends: Integrating new technologies to foster student interaction and collaboration. Distance Education, 27(2), 139-153. doi.org/10.1080/01587910600789498
  • Bonk, C. J. (2020). Pandemic ponderings, 30 years to today: synchronous signals, saviors, or survivors?. Distance Education, 41(4), 589-599. doi.org/10.1080/01587919.2020.1821610
  • Chaka, J. G., & Govender, I. (2017). Students’ perceptions and readiness towards mobile learning in colleges of education: a Nigerian perspective. South African Journal of Education, 37(1), 1-12. doi. 10.15700/saje.v37n1a1282
  • Chen, P. Y., & Hwang, G. J. (2019). An empirical examination of the effect of self-regulation and the Unified Theory of Acceptance and Use of Technology (UTAUT) factors on the online learning behavioural intention of college students. Asia Pacific Journal of Education, 39(1), 79-95. doi.org/10.1080/02188791.2019.1575184
  • Chin, W. W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. In G. A. Marcoulides (Ed.), Modern methods for business research içinde (ss. 295–336). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.
  • Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201. doi.org/10.1016/j.im.2008.02.003
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in Human Behavior, 49, 272-281. doi.org/10.1016/j.chb.2015.03.022
  • Diehl, W. (2020). Opportunities and change amidst debate, confusion, and challenges in education. American Journal of Distance Education, 34(4), 259-259. doi.org/10.1080/08923647.2020.1853424
  • Fianu, E., Blewett, C., & Ampong, G. O. (2020). Toward the development of a model of student usage of MOOCs. Education + Training, 62(5), 521–541. Retrived from https://www.emerald.com/insight/content/doi/10.1108/ET-11-2019-0262/full/html
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. doi.org/10.1177/002224378101800104
  • Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review.
  • Hair Jr. F., Hult, G.T.M., Ringle, C.M. & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling. Sage, Thousand Oaks, CA.
  • Hair, J. F. Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. doi.org/10.2753/MTP1069-6679190202
  • Henseler J.,& Fassott G. (2010) Testing Moderating Effects in PLS Path Models: An Illustration of Available Procedures. In V. Esposito Vinzi, W. W. Chin, J. Henseler & H. Wang (Eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics (pp. 713-735). Springer, Berlin, Heidelberg.
  • Henseler, J., Ringle, C. M., and Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. in new challenges to international marketing. Bingley: Emerald Group Publishing Limited.
  • Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91-112. doi.org/10.1080/07421222.1999.11518247
  • Karasar, N. (2009). Bilimsel araştırma yöntemi (19. Baskı). Ankara: Nobel
  • Kharma, Q. (2019). Investigating students ‘acceptance of online courses at Al-Ahliyya Amman University. International Journal of Advanced Computer Science and Applications, 10(7), 202-208.
  • Lakhal, S., & Khechine, H. (2021). Technological factors of students’ persistence in online courses in higher education: The moderating role of gender, age and prior online course experience. Education and Information Technologies, 1-27. https://doi.org/10.1007/s10639-020-10407-w
  • Lee, L., Petter, S., Fayard, D., & Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305-328. doi.org/10.1016/j.accinf.2011.05.002
  • Mahande, R. D., & Malago, J. D. (2019). An e-learning acceptance evaluation through UTAUT model in a postgraduate program. Journal of Educators Online, 16(2), n2. Retrieved from https://eric.ed.gov/?id=EJ1223779
  • Mulik, S., Srivastava, M., & Yajnik, N. (2018). Extending UTAUT model to examine MOOC adoption. NMIMS Management Review, XXXVI(2), 26-44.
  • Naidu, S. (2020) It is the worst—and the best—of times!. Distance Education, 41(4), 425-428. doi.org/10.1080/01587919.2020.1825929
  • Odegbesan, O. A., Ayo, C., Oni, A. A., Tomilayo, F. A., Gift, O. C., & Nnaemeka, E. U. (2019, August). The prospects of adopting e-learning in the Nigerian education system: a case study of Covenant University. In Journal of Physics: Conference Series (Vol. 1299, No. 1, p. 012058). IOP Publishing.
  • Putri, V. Q., Shihab, M. R., & Hidayanto, A. N. (2019, October). Does inertia effect e-learning system acceptance among university lecturers? Insights from Sriwijaya University. In 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS) (pp. 307-312). IEEE.
  • Radovan, M., & Kristl, N. (2017). Acceptance of technology and ıts ımpact on teachers' activities in virtual classroom: ıntegrating UTAUT and CoI into a Combined Model. Turkish Online Journal of Educational Technology-TOJET, 16(3), 11-22. Retrieved from https://files.eric.ed.gov/fulltext/EJ1152624.pdf
  • Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2020). Social isolation and acceptance of the learning management system (LMS) in the time of covıd-19 pandemic: an expansion of the UTAUT model. Journal of Educational Computing Research, 59(2),183-208. doi.org/10.1177/0735633120960421
  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.
  • Salloum, S. A., & Shaalan, K. (2018, September). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In International Conference on Advanced Intelligent Systems and Informatics (pp. 469-480). Springer, Cham.
  • Samsudeen, S.N., & Mohamed, R. (2019), University students’ intention to use e-learning systems: a study of higher educational institutions in Sri Lanka, Interactive Technology and Smart Education, 16(3), 219-238. Retrieved from https://www.emerald.com/insight/content/doi/10.1108/ITSE-11-2018-0092/full/html
  • Sattari, A., Abdekhoda, M., & Zarea Gavgani, V. (2017). Determinant factors affecting the web–based training acceptance by health students, applying UTAUT model. International Journal of Emerging Technologies in Learning. 12(10), 112-126.doi.org/10.3391/ijet.v12i10.7258
  • Shaqrah, A. A. (2015). Explain the behavior intention to use e-learning technologies: A unified theory of acceptance and use of technology perspective. International Journal of Web-Based Learning and Teaching Technologies, 10(4), 19-32. doi.10.4018/IJWLTT.2015100102
  • Šumak, B., Polancic, G., & Hericko, M. (2010, February). An empirical study of virtual learning environment adoption using UTAUT. In 2010 Second international conference on mobile, hybrid, and on-line learning (pp. 17-22). IEEE.
  • Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 1-12. doi. 2158244013503837.
  • Telli, S. & Altun, D. (2020). Coronavirüs ve çevrimiçi (online) eğitimin önlenemeyen yükselişi. Üniversite Araştırmaları Dergisi, 3(1), 25-34. doi.org/10.32329/uad.711110
  • Thowfeek, M.H. & Jaafar, A. (2013) An investigation of the factors that influence students’ intention to adopt e-learning. In H.B. Zaman, P. Robinson, P. Olivier, T.K. Shih ve S. Velastin (Eds.) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science: Springer, Cham.
  • Umrani-Khan, F., & Iyer, S. (2009, July). ELAM: A model for acceptance and use of e-learning by teachers and students. In Proceedings of the International Conference on e-Learning (pp. 475-485). Institute of Technology Bombay, Mumbai, India UNESCO, (2021b). UNESCO figures show two thirds of an academic year lost on average worldwide due to Covid-19 school closures. (Erişim: 13.02.2021), https://en.unesco.org/news/unesco-figures-show-two-thirds-academic-year-lost-average-worldwide-due-covid-19-school
  • UNESCO, (2021a). Education: From disruption to recovery. (Erişim: 13.02.2021), https://en.unesco.org/covid19/educationresponse
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi.org/10.2307/30036540
  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of ınformation technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. doi.org/10.2307/41410412
  • Wang, M. H. (2016). Factors influencing usage of e-learning systems in Taiwan's public sector: applying the UTAUT Model. Advances in Management and Applied Economics, 6(6), 63-82. Retrieved from http://www.scienpress.com/Upload/AMAE/Vol%206_6_5.pdf
  • Yakubu, M. N., & Dasuki, S. I. (2019). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492–502. doi.org/10.1177/0266666918765907
  • Yükseköğretim Kurulu, (2020). Pandemi Günlerinde Türk Yükseköğretimi. (Erişim: 13.02.2021), https://covid19.yok.gov.tr/Sayfalar/HaberDuyuru/pandemi-gunlerinde-turk-yuksekogretimi.aspx

Ayrıntılar

Birincil Dil Türkçe
Konular Eğitim, Eğitim Araştırmaları
Bölüm Makaleler
Yazarlar

Betül ÖZAYDIN ÖZKARA> (Sorumlu Yazar)
Isparta Uygulamalı Bilimler Üniversitesi, Uzaktan Eğitim Meslek Yüksekokulu
0000-0002-2011-1352
Türkiye


Hanife ÇİVRİL>
Isparta Uygulamalı Bilimler Üniversitesi, Uzaktan Eğitim Meslek Yüksekokulu
0000-0003-2925-3688
Türkiye


Emine ARUĞASLAN>
Isparta Uygulamalı Bilimler Üniversitesi, Uzaktan Eğitim Meslek Yüksekokulu
0000-0002-8153-9117
Türkiye

Yayımlanma Tarihi 22 Ağustos 2022
Yayınlandığı Sayı Yıl 2022, Cilt 18, Sayı 2

Kaynak Göster

APA Özaydın Özkara, B. , Çivril, H. & Aruğaslan, E. (2022). Üniversite Öğrencilerinin Uzaktan Eğitimi Kullanım Niyetlerinin UTAUT Bağlamında İncelenmesi . Mersin Üniversitesi Eğitim Fakültesi Dergisi , 18 (2) , 132-153 . DOI: 10.17860/mersinefd.991686

The content of the Mersin University Journal of the Faculty of Education is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.