ISSN: 2226-6348
Open access
This article review works to specifically define the motivational determinants for students engaged in online learning as well as the difficulties of educators as a primary source for disseminating learning during the COVID-19 pandemic. Ironically, the pandemic has contributed to some improvement in the global education system where online learning is increasingly growing in terms of use. Despite the online learning adoption trend, there is a problem with learners determined to participate in online learning When addressing students' desire to engage in online learning, the motivation elements of the student itself should be calculated. Students' acceptance of technology, self-efficacy, and self-determination are among the elements that should be examined. In addition, educators must consider the complexities of integrating online teaching and learning. While most educators are not completely prepared for the rapid transition, they need to face challenges. In view of the challenges, the development of conducive online communication, the adequacy of online teaching technology skills, the positive attitude towards technology, the need for training, self-motivation boost and the support of student participation in online learning are appropriate. To build a resilient education system, a lot would have to be tackled in the future and additional study should be done.
Adam, F. (2020). How common is working from home?. Retrieved from
http//www.krinstitute.org/assets/contentMS/img/template/editor/20200324_Views_COVID %20AMMF%20v3a.pdf.
Agina, A. M., Kommers, P. A., & Steehouder, F. (2011). The effect of the external regulator’s absence on children’s speech use, manifested self-regulation, and task performance during learning tasks. Comput. Hum. Behav. 27, 1118–1128. doi:
10.1016/j.chb.2010.10.007
Allen, I. E., Seaman, J., Poulin, R., & Straut, T. T. (2016). Online report card: Tracking online education in the United States. Babson survey research group and the online learning consortium (OLC), Pearson, and WCET state authorization Network.
Artino, A. R. (2008), “Motivational beliefs and perceptions of instructional quality: predicting satisfaction with online training”, Journal of Computer Assisted Learning, 24(3), 260-270.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman.
Bates, A. W., & Sangra, A. (2011), Managing Technology in Higher Education, Jossey-Bass, NJ.
Bower, M. (2019). Technology ?mediated learning theory. British Journal Education Tech- nology, 50, 1035–1048. 10.1111/bjet.12771 .
Brecht, H. D., & Ogilby, S. M. (2008). Enabling a comprehensive teaching strategy: Video lectures, Journal of Information Technology Education, 7, 71-86.
Castle, S. R., & McGuire, C. (2010), “An analysis of student self-assessment of online, blended, and faceto-face learning environments: implications for sustainable education delivery”, International Education Studies, 3(3), 36-40.
ChanLin, L. J. (2005). Development of a Questionnaire for Determining the Factors in Technology Integration among Teachers. Journal of Instructional Psychology, 32(4), 287-292. Retrieved from https://www.learntechlib.org/p/98822/
Chen, C. M., & Wu, C. H. (2015). Effects of different video lecture types on sustained attention, emotion, cognitive load and learning performance. Computer & Education, 80, 108-121.
Chen, F., Myers, B., & Yaron, D. (2000). Using handheld devices for test in classes. CMU-CS-00-152.
Christopher, C., & Weng, W. (2020). http://www.krinstitute.org/Views-@- Mitigating_ the_Economic_Shocks_of_Covid-19-; Protect_our_Wo rkers!_.aspx
Davis, F. D. (1989), “Perceived usefulness, perceived ease of use and user acceptance of information technology”, MIS Quarterly, 13(3), 319-340.
De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching instructional use and the technology acceptation of learning management systems by secondary school teachers. Computers & Education, 58(2), 688-696. https://doi.org/10.1016/j.compedu.2011.09.013
Deci, E. L., Ryan, R. M. (1985). Intrinsic motivation and self determination in human behavior. New York: Plenum Press.
Demetriadis, S., Barbas, A., Molohides, A., Palaigeorgiou, G., Psillos, D., Vlahavas, I., & Pombortsis, A. (2003). “Cultures in negotiation”: teachers’ acceptance/resistance attitudes considering the infusion of technology into schools. Computers & Education, 41(1), 19–37. doi:10.1016/s0360-1315(03)00012-5
Fletcher, T., & Bullock, S. M. (2015), “Reframing pedagogy while teaching about teaching online: a collaborative self-study”, Professional Development in Education, 41(4), 690-706.
Gedera, D. S. P., & Williams, P. J. (2013). Using Activity Theory to understand contradictions in an online university course facilitated by Moodle. International Journal of Information Technology and Computer Science (IJITCS), 10(1), 32–40.
Gedera, D. S. P., Williams, P. J., & Wright, N. (2015). Identifying Factors Influencing Student’s Motivation and Engagement in Online Courses. Bussiness Media Singapore Science ,13-23.
Gray, K., & Tobin, J. (2010), “Introducing an online community into a clinical education setting: a pilot study of student and staff engagement and outcomes using blended learning”, BMC Medical Education, 10(1), 1-16.
Hadad, W. (2007). ICT-in-education toolkit reference handbook. InfoDev. Retrieved from http://www.infodev.org/en/Publication.301.html)
Hartnett, M., St. George, A., Dron, J. (2011). Examining motivation in online distance learning environments: Complex, multifaceted and situation-dependent. The International Review of Research in Open and Distributed Learning. 12(6): 20-38.
Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080-1090. https://doi.org/ 10.1016/j.compedu.2010.05.004
Hwang, G. J., Lai, C. L., & Wang, S. Y. (2015). Seamless flipped learning: a mobile technology-enhanced flipped classroom with effective learning strategies. Journal of Computers in Education, 2(4), 449–473. https://doi.org/10.1007/s40692-015-0043-0
Jimoyiannis, A., & Komis, V. (2007). Examining teachers’ beliefs about ICT in education: implications of a teacher preparation program. Teacher Development, 11(2), 149–173. doi:10.1080/13664530701414779
Lee, W., Reeve, J., Xue, Y., Xiong, J. (2012). Neural differences between intrinsic reasons for doing versus extrinsic reasons for doing: An fMRI study. Neuroscience Research. 73(1), 68-72
Lee, Y. H., Hsiao, C., & Purnomo, S. H. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5), 562-279. https://doi.org/10.14742/ajet.381
Martin, J. M. (2012). Relationship of customer trait and situational factor determinants with the technology acceptance of self-service. Doctoral dissertation. Capella University)
McIntyre, S. (2011). Learning to teach online: Engaging and motivating students. http://www. academia.edu/612888/Engaging_and_motivating_students.
McIsaac, M. S., Blocher, M. J., Mahes, V., & Vrasidas, C. (2006). Student and teacher perceptions of interaction in online computer-mediated communication. Educational Media International, 36(2), 121–131.
Morillo, J. P., Reigal, R. E., Hernandez-Mendo, A. (2018). Motivational Orientations, Autonomy Support, and Psychological Needs in Beach Handball. Revista Internacional de Medicina y Ciencias de la Actividad Física y el Deporte. 18(69), 103-117.
Ormrod, J. E. (2011). Social cognitive views of learning. In Educational psychology: Developing learners, ed. P. A. Smith, 352–354. Boston: Pearson
Paechter, M., & Maier, B. (2010). Online or face-to-face? Students' experiences and preferences in e-learning. The Internet and Higher Education, 13(4), 292-297. https://doi.org/10.1016/j.iheduc.2010.09.004
Park, J., and H. J. Choi. 2009. Factors influencing adult learners’ decision to dropout or persist in online learning. Journal of Educational Technology & Society 12 (4),207–217.
Premchaiswadi, W., Porouhan, P., & Premchaiswadi, N. (2012). An empirical study of the key success factors to adopt e-learning in Thailand. In International conference on information society (i-Society 2012), 333-338. IEEE.
Purnomo, S.H., & Lee, Y.H. (2013). E-learning adoption in the banking workplace in Indonesia: an empirical study. Information Development, 29(2), 138-153.
https://doi.org/10.1177/0266666912448258
Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., Koole, M. (2020). Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity. Postdigital Sci. Educ., 1–23.
Rezaei, M., Mohammadi, H. M., Asadi, A., & Kalantary, K. (2008). Predicting e-learning application in agricultural higher education using technology acceptance model. Turkish Online Journal of Distance Education, 9(1), 85-95.
https://dergipark.org.tr/tr/pub/tojde/issue/16915/176477
Schechter, E. L. (2000). Factors relating to classroom implementation of computer technology in elementary schools (pp. 1-128). St. John's University (New York), School of Education and Human Services. Retrieved from https://www.learntechlib.org/p/123270/
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2016). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing coun- tries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 3 (25), 306–328. 10.1080/10494820.2015.1122635
Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the Technology Acceptance Model (TAM). Journal of Computer Assisted Learning, 24(2), 128-143. Doi:10.1111/j.1365-2729.2007.00247.x
Wan Hassan, W. A. S., Ariffin, A., Ahmad, F., Sharberi, S. N. M., Azizi, N. M. I., & Zulkiflee, S. N. (2020). COVID-19 Pandemic: Langkawi Vocational College Student Challenge in Using Google Classroom for Teaching and Learning (T&L) International Journal of Advanced Trends in Computer Science and Engineering, 9(2), 3299 – 3307.
Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34 (3), 302–323
Wilde, N., Hsu, A. (2019). The influence of general self-efficacy on the interpretation of vicarious experience information within online learning. International Journal of Educational Technology in Higher Education, 16(1), 1-20.
In-Text Citation: (Zakirai@Zakaria et al., 2021)
To Cite this Article: Zakirai@Zakaria, N., Eshak, E. S., Jidi, M. M., & Jamian, N. F. (2021). Online Learning at Tertiary Institution During Pandemic of Covid-19: A Conceptual Review of Students’ Motivational Issues and Educators’ Challenges. International Journal of Academic Research in Business and Social Sciences, 11(11), 1218–1228.
Copyright: © 2021 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode