ejosat Avrupa Bilim ve Teknoloji Dergisi 2148-2683 Osman SAĞDIÇ 10.31590/ejosat.990073 Engineering Mühendislik COVID-19 Pandemisi Sırasında Teknoloji ve Hizmetlerin Öğrenme Sistemlerine Etkisi The Effect of Technology and Service on Learning Systems During the COVID-19 Pandemic https://orcid.org/0000-0002-7740-2206 Ullah Arıfullah Universiti Tun Hussein Onn Malaysia https://orcid.org/0000-0002-2955-6761 Batur Dinler Özlem SİİRT ÜNİVERSİTESİ https://orcid.org/0000-0002-2131-6368 Batur Şahin Canan MALATYA TURGUT OZAL UNIVERSITY 11 30 2021 28 106 114 09 02 2021 09 03 2021 Copyright © 2013, Avrupa Bilim ve Teknoloji Dergisi 2013 Avrupa Bilim ve Teknoloji Dergisi

Arka Plan/Amaç: Yeni tip koronavirüs hastalığı ilk olarak Çin'in Wuhan eyaletinde Aralık 2019'da ortaya çıktı. Dünya Sağlık Örgütü (DSÖ), bu hastalığı Şubat 2020'de bir pandemi ilan etti. Bu pandemi tüm dünyada başta sağlık olmak üzere eğitim, ekonomi, ticaret, iş hayatı, sosyal hayat gibi birçok alanı önemli ölçüde etkilemiştir. Bu bağlamda, bu çalışma COVID-19'un eğitim sistemi üzerine etkisini araştırmaktadır. COVID-19 pandemisinin dünyadaki eğitim sistemleri üzerine etkisi büyük değişikliklere yol açmış, tüm müfredatı çevrimiçi yaklaşımlarla e-öğrenme sistemlerine kaydırmıştır. Çalışma, üniversitelerin öğrencilerinin COVID-19 koşullarında eğitimlerini kolaylaştırmak için bir e-öğrenme platformu geliştirmelerine yardımcı olmayı ve COVID-19 pandemisi sonrasında teknoloji ve hizmetlerin öğrenme sistemlerindeki rolünün ne olacağını değerlendirmeyi amaçlamıştır. Yaklaşımlar: Bu anlamda Fas'taki farklı üniversitelerden gelen anketlere dayalı olarak bir inceleme yapılmıştır. Danışılan üniversite sayısı, örneklem, dahil etme ve hariç tutma kriterleri, çalışılan örneklemin demografik özellikleri, elde edilen sonuçların istatistiksel analizi, farklı öneriler ve öğrenme sistemleri uygulamaları tartışılmıştır. Sonuç ve gelecekteki araştırmalar: Bu makale, pandemi ve herhangi bir afet sırasında e-öğrenme sistemlerinin gelişimine ışık tutmuş, farklı akademik kurumlara e-öğrenme ile ilgili bu zorluklarla nasıl başa çıkılacağı konusunda önerilerde bulunmuştur.

Background/Objectives: The new type of coronavirus disease first appeared in the Wuhan province of China in December 2019. The World Health Organization (WHO) declared this disease a pandemic in February 2020. This pandemic has significantly affected many areas such as education, economy, trade, business life, social life, and primarily health all over the world. In this context, the present study investigates the effect of COVID-19 on the education system. The effect of the COVID-19 pandemic on education systems across the world has caused major changes and shifted the entire curriculum to e-learning systems through online approaches. The study aimed to help universities develop an e-learning platform to facilitate the education of their students under COVID-19 conditions and assess what the role of technology and services would be in learning systems after the COVID-19 pandemic. Approaches: To this end, a review was conducted based on questionnaires from different universities in Morocco. The number of universities consulted, samples, inclusion and exclusion criteria, demographic characteristics of the studied samples, statistical analysis of the obtained results, different suggestions and implementations of learning systems were discussed. Conclusion and future research: This paper shed some light on the growth of e-learning systems during the pandemic and any disasters and provided suggestions for different academic institutions on how to deal with these challenges associated with e-learning.

 COVID-19 Technology Learning System Effect of COVID-19 Internet COVID-19 Teknoloji Öğrenme Sistemi COVID-19’un etkisi İnternet Markauskaite, L. (2006), Towards an integrated analytical framework of information and communications technology literacy: from intended to implemented and achieved dimensions, Information Research: an International electronic journal, 11(3):1-25. Huang, W., & Mille, A. (2006), ConKMeL: a contextual knowledge management framework to support multimedia e-Learning, Multimedia Tools and Applications, 30(2):205-219. https://doi.org/10.1007/s11042-006-0024-4. Caldwell, J. E. (2007), Clickers in the large classroom: Current research and best-practice tips, CBE—Life Sciences Education, 6(1): 9-20. https://doi.org/10.1187/cbe.06-12-0205. Martin, A. (2008), Digital literacy and the “digital society”, Digital literacies: Concepts, policies and practices, 30, 151-176. Riddell, S., & Weedon, E. (2014), Disabled students in higher education: Discourses of disability and the negotiation of identity, International Journal of Educational Research, 63, 38-46. https://doi.org/10.1016/j.ijer.2013.02.008. Logica, B., & Magdalena, R. (2015), Using big data in the academic environment, Procedia Economics and Finance, 33, 277-286. https://doi.org/10.1016/S2212-5671(15)01712-8. Nordin, N. B., Mir, R. N., & Noor, Z. (2017), Adoption of cloud computing in higher learning institutions: a systematic review, Indian Journal of Science and Technology, 10(36), 1-19, DOI: 10.17485/ijst/2017/v10i36/117641. Wang, Y., Kung, L., & Byrd, T. A. (2018), Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13. https://doi.org/10.1016/j.techfore.2015.12.019. Ullah, A.(2019), Artificial bee colony algorithm used for load balancing in cloud computing. IAES International Journal of Artificial Intelligence, 8(2), 156. DOI: 10.11591/ijai.v8.i2.pp156-167. Vu, T. N. (2020), Examining Teacher Agency Among Teacher Educators: An Action Research In Vietnam, Australian Journal of Teacher Education, 45(7), 6, DOI: 10.14221/ajte.2020v45n7.6. Lasuen, U. O., Iragorri, M. A. O., & Diez, J. R. (2020), Towards energy transition at the Faculty of Education of Bilbao (UPV/EHU): diagnosing community and building, International Journal of Sustainability in Higher Education. https://doi.org/10.1108/IJSHE-12-2019-0363. Udok, M. B., Eton, C. U., & Akpanika, E. N. (2020), Coronavirus Pandemic and its Effect on African Religiosity, International Journal of Humanities and Innovation (IJHI), 3(3), 109-114. https://doi.org/10.33750/ijhi.v3i3.86. Ullah, A., & Nawi, N. M. (2020), Enhancing the dynamic load balancing technique for cloud computing using HBATAABC algorithm. International Journal of Modeling, Simulation, and Scientific Computing, 2050041. https://doi.org/10.1142/S1793962320500415. Maphalala, M. C., & Adigun, O. T. (2020), Academics’ Experience of Implementing E-Learning in a South African Higher Education Institution, International Journal of Higher Education, 10(1), 2021. doi:10.5430/ijhe.v10n1p1. Sangster, A., Stoner, G., & Flood, B. (2020), Insights into accounting education in a COVID-19 world, Accounting Education, 29(5), 431-562. https://doi.org/10.1080/09639284.2020.1808487. Batur Dinler, Ö., Batur Şahin, C. (2021), Prediction of phishing web sites with deep learning using WEKA environment. European Journal of Science and Technology, (2021), 24, 35-41. https://doi.org/10.31590/ejosat.901465. Hofmeister, C., & Pilz, MUsing E-Learning to Deliver In-Service Teacher Training in the Vocational Education Sector: Perception and Acceptance in Poland, Italy and Germany. Education Sciences, . (2020). 10(7), 182. https://doi.org/10.3390/educsci10070182. Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Postdigital Science and Education, (2020). 2(3), 923-945. https://doi.org/10.1007/s42438-020-00155-y. Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality & Quantity, (2020). 1-22. https://doi.org/10.1007/s11135-020-01028-z. Ullah, A., Nawi, N. M., Shahzad, A., Khan, S. N., & Aamir, M. (2017), An e-learning system in Malaysia based on green computing and energy level, JOIV: International Journal on Informatics Visualization, 1(4-2), 184-187. http://dx.doi.org/10.30630/joiv.1.4-2.63. De Amicis, R., Riggio, M., Badr, A. S., Fick, J., Sanchez, C. A., & Prather, E. A. (2019), Cross-reality environments in smart buildings to advance STEM cyberlearning, International Journal on Interactive Design and Manufacturing (IJIDeM), 13(1), 331-348. Crawford, J., Butler-Henderson, K., Rudolph, J., Malkawi, B., Glowatz, M., Burton, R., & Lam, S. (2020), COVID-19: 20 countries' higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3(1), 1-20. http://dx.doi.org/10.37074/jalt.2020.3.1.7. Azevedo, J. P., Hasan, A., Goldemberg, D., Iqbal, S. A., & Geven, K. (2020), Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes: A set of global estimates. https://doi.org/10.1596/1813-9450-9284. Harris, B. N., McCarthy, P. C., Wright, A. M., Schutz, H., Boersma, K. S., Shepherd, S. L., & Ellington, R. M. (2020), From panic to pedagogy: Using online active learning to promote inclusive instruction in ecology and evolutionary biology courses and beyond. Ecology and Evolution. https://doi.org/10.1002/ece3.6915. Shehzadi, S., Nisar, Q. A., Hussain, M. S., Basheer, M. F., Hameed, W. U., & Chaudhry, N. I. (2020), The role of digital learning toward students' satisfaction and university brand image at educational institutes of Pakistan: a post-effect of COVID-19. Asian Education and Development Studies. https://doi.org/10.1108/AEDS-04-2020-0063. [26] Ali, F., Zhou, Y., Hussain, K., Nair, P. K., & Ragavan, N. A. (2016), Does higher education service quality effect student satisfaction, image and loyalty?. Quality assurance in education. https://doi.org/10.1108/QAE-02-2014-0008. Orel, F. D., & Kara, A. (2014), Supermarket self-checkout service quality, customer satisfaction, and loyalty: Empirical evidence from an emerging market. Journal of Retailing and Consumer Services, 21(2),118-129. https://doi.org/10.1016/j.jretconser.2013.07.002. Lowry, P. B., & Gaskin, J. (2014), Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE transactions on professional communication,57(2), 123-146. https://doi.org/10.1109/TPC.2014.2312452. Cole, D. A., Maxwell, S. E., Arvey, R., & Salas, E. (1993), Multivariate group comparisons of variable systems: MANOVA and structural equation modeling. Psychological Bulletin, 114 (1), 174. https://psycnet.apa.org/doi/10.1037/0033-2909.114.1.174. Musil, C. M., Jones, S. L., & Warner, C. D. Structural equation modeling and its relationship to multiple regression and factor analysis. Research in Nursing & Health, (1998). 21(3), 271-281.https://doi.org/10.1002/(SICI)1098-240X(199806)21:3%3C271::AID-NUR10%3E3.0.CO;2-G. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016), A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications. Ulaganathan, M., Nithya, R., & Rajendran, S. (2012), Surface analysis studies on polymer electrolyte membranes using scanning electron microscope and atomic force microscope. In Scanning Electron Microscopy. Intechopen. (2012). DOI: 10.5772/34948. Yang, R., Zhang, S., Zhang, L., & Liu, W. (2013), Electrical properties of composite polymer electrolytes based on PEO-SN-LiCF3SO3. Int. J. Electrochem. Sci, 8, 10163-10169. Gajendran, N. (2020), Blockchain-Based secure framework for elearning during COVID-19. Indian journal of science and technology, 13(12), 1328-1341. https://doi.org/10.17485/IJST/v13i12.152. Sathishkumar, V. & Radha, R. & Saravanakumar, Ar & Mahalakshmi, K.. (2020), E-Learning during Lockdown of Covid-19 Pandemic: A Global Perspective. International Journal of Control and Automation. 13. 1088-1099. Mpungose, C.B.(2020), Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic. Humanit Soc Sci Commun 7, 113. https://doi.org/10.1057/s41599-020-00603-x. Al-Balas, M., Al-Balas, H.I., Jaber, H.M. et al. (2020), Distance learning in clinical medical education amid COVID-19 pandemic in Jordan: current situation, challenges, and perspectives. BMC Med Educ 20, 341. https://doi.org/10.1186/s12909-020-02257-4. Armstrong-Mensah, Elizabeth; Ramsey-White, Kim; Yankey, Barbara; Self-Brown, Shannon (2020), COVID-19 and Distance Learning: Effects on Georgia State University School of Public Health Students. Frontiers in Public Health, 8(), 576227–. doi:10.3389/fpubh.2020.576227. Mulla, Z. D., Osland-Paton, V., Rodriguez, M. A., Vazquez, E., & Kupesic Plavsic, S. (2020), Novel coronavirus, novel faculty development programs: rapid transition to eLearning during the pandemic, Journal of Perinatal Medicine, 48(5), 446-449. doi: https://doi.org/10.1515/jpm-2020-0197. Nugroho, R. A., Basari, A., Suryaningtyas V.W., & Cahyono, S.P. (2020), University Students’ Perception of Online Learning in Covid-19 Pandemic: A Case Study in a Translation Course, 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, pp. 225-231, doi: 10.1109/iSemantic50169.2020.9234251. Murphy, M. P. A. (2020), COVID-19 and emergency eLearning: Consequences of the securitization of higher education for post-pandemic pedagogy, Contemporary Security Policy, 41:3, 492-505, DOI: 10.1080/13523260.2020.1761749. Oztemel, E., & Gursev, S. (2020), Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127-182. Bao, W. (2020), COVID‐19 and online teaching in higher education: A case study of Peking University. Hum Behav & Emerg Tech. 2: 113– 115. https://doi.org/10.1002/hbe2.191. Yanti, B., Wahyudi, E., Wahiduddin, W., Novika, R. G. H., Arina, Y. M. D. A., Martani, N. S., & Nawan, N. (2020), Community knowledge, attitudes, and behavior towards social distancing policy as prevention transmission of COVID-19 in indonesia. Jurnal Administrasi Kesehatan Indonesia, 8(2), 4-14. Sugarman, J. R., Warren, C. W., Oge, L., & Helgerson, S. D. (1992), Using the Behavioral Risk Factor Surveillance System to monitor year 2000 objectives among American Indians. Public Health Reports, 107(4), 449. Moore, L. V., Dodd, K. W., Thompson, F. E., Grimm, K. A., Kim, S. A., & Scanlon, K. S. (2015), Using behavioral risk factor surveillance system data to estimate the percentage of the population meeting US Department of Agriculture food patterns fruit and vegetable intake recommendations. American journal of epidemiology, 181(12), 979-988. Wijaya, I. K. (2012), Environmental Influences Cause Stress on the Use of Computer. International Journal of Public Health Science, 1(1), 7253. Boadu, R. O., Agyei-Baffour, P., & Edusei, A. K. (2019), Data accuracy and completeness of monthly midwifery returns indicators of Ejisu Juaben Health Directorate of Ghana. International Journal of Public Health Science, 8(1), 106-117.